{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_normal_distribution(var):\n",
    "    \"\"\"\n",
    "    Sample normal distribution with variance = var\n",
    "    \"\"\"\n",
    "    rand = np.random.uniform(low=-1.0, high=1.0, size=12)\n",
    "    return var * np.sum(rand) / 6\n",
    "\n",
    "\n",
    "def prob_normal_distribution(x, var):\n",
    "    \"\"\"\n",
    "    Calculate value of PDF of p(X = x) given X having normal distribution\n",
    "    \"\"\"\n",
    "    return np.exp(-x**2 / (2 * var)) / np.sqrt(2 * np.pi * var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.998518184845532"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test value of PDF\n",
    "dx = 0.01\n",
    "x_arr = [-1 + i * dx for i in range(201)]\n",
    "prob = np.array([prob_normal_distribution(x, 0.1) * dx for x in x_arr])\n",
    "np.sum(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def motion_model(x, u):\n",
    "    \"\"\"\n",
    "    Compute motion model\n",
    "    Input:\n",
    "        x (np.ndarray) - shape (3,1) : robot location in 3D space\n",
    "        u (np.ndarray) - shape (3,1) : motion command (robot velocities)\n",
    "    Output:\n",
    "        xt (np.ndarray) - shape (3,1) : new robot location\n",
    "    \"\"\"\n",
    "    return x + u * delta_t\n",
    "    \n",
    "\n",
    "def measurement_model(x, m):\n",
    "    \"\"\"\n",
    "    Compute measurement model given state x (robot loc.) & map m\n",
    "    Input:\n",
    "        x (np.ndarray) - shape (3,1) : robot location in 3D space\n",
    "        m (np.ndarray) - shape (num_beacons,3) : location of all beacons in the map\n",
    "    Output:\n",
    "        z (np.ndarray) - shape (num_beacons,) : distance from robot to each beacons\n",
    "    \"\"\"\n",
    "    return np.sqrt(np.sum((m - x.squeeze())**2, axis=1))\n",
    "\n",
    "\n",
    "def simulation_step(x, u):\n",
    "    \"\"\"\n",
    "    Perform 1 step of simulation\n",
    "    Input:\n",
    "        x (np.ndarray) - shape (3,1) : robot location in 3D space\n",
    "        u (np.ndarray) - shape (3,1): motion command (robot velocities)\n",
    "    Output:\n",
    "        xt (np.ndarray) - shape (3,1) : new robot location\n",
    "        zt (np.ndarray) - shape (num_beacons,) : measurement at new location\n",
    "    \"\"\"\n",
    "    # Perturb motion command\n",
    "    u_noise = np.array([sample_normal_distribution(var_v) for i in range(3)])\n",
    "    u_hat = u + u_noise.reshape(-1, 1)\n",
    "    \n",
    "    # Compute new location\n",
    "    xt = motion_model(x, u_hat)  # here xt is the ground truth of robot location\n",
    "    \n",
    "    # Compute expected value of measurement\n",
    "    zt_bar = measurement_model(xt, m) \n",
    "    # Perturb measurement\n",
    "    zt = zt_bar + np.array([sample_normal_distribution(var_z) for i in range(num_beacons)])\n",
    "    \n",
    "    return xt, zt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_motion_model(xt, ut, x_tm1):\n",
    "    \"\"\"\n",
    "    Probability of state xt conditioned on action ut & previous state x_tm1\n",
    "    Input:\n",
    "        xt (np.ndarray) - shape (3,1) : new robot location in 3D space\n",
    "        ut (np.ndarray) - shape (3,1): motion command (robot velocities)\n",
    "        x_tm1 (np.ndarray) - shape (3,1) : previous robot location in 3D space\n",
    "    Return\n",
    "        p(xt | ut, x_tm1) - scalar\n",
    "    \"\"\"\n",
    "    real_velocity = (xt - x_tm1) / delta_t\n",
    "    diff = (real_velocity - ut).squeeze()  # value of motion command noise\n",
    "    return prob_normal_distribution(diff[0], var_v) * prob_normal_distribution(diff[1], var_v) * prob_normal_distribution(diff[2], var_v) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prob_measurement_model(zt, xt, m):\n",
    "    \"\"\"\n",
    "    Probability of measurement zt conditioned on state xt & map m\n",
    "    Input:\n",
    "        zt (np.ndarray) - shape (num_beacons,) : latest measurement\n",
    "        xt (np.ndarray) - shape (3,1) : current robot location in 3D space\n",
    "        m (np.ndarray) - shape (num_beacons,m) : location of all beacons in the map\n",
    "    Return\n",
    "        p(zt | xt, m) - scalar\n",
    "    \"\"\"\n",
    "    # calculate expected measurement\n",
    "    z_hat = measurement_model(xt, m)\n",
    "    # measurement noise\n",
    "    noise = zt - z_hat\n",
    "    # probability of measurement\n",
    "    prob = 1.\n",
    "    for i in range(num_beacons):\n",
    "        prob *= prob_normal_distribution(noise[i], var_z)\n",
    "    return prob\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_localization(p_tm1, ut, zt, m):\n",
    "    \"\"\"\n",
    "    Implementation of grid localization algorithm\n",
    "    Input:\n",
    "        p_tm1 (np.ndarray) - (num_cells,): each element store coordinate of cell's COM (first 3) &\n",
    "                                            cell probability\n",
    "        ut (np.ndarray) - shape (3,1): motion command (robot velocities)\n",
    "        zt (np.ndarray) - shape (num_beacons,) : latest measurement\n",
    "        m (np.ndarray) - shape (num_beacons,m) : location of all beacons in the map\n",
    "    \"\"\"\n",
    "    p_t = np.zeros(len(com_list))\n",
    "    for k, xk in enumerate(com_list):\n",
    "        # prediction\n",
    "        p_xk = np.array([prob_motion_model(xk, ut, xi) for xi in com_list])\n",
    "        p_bar = np.sum(p_xk * p_tm1)\n",
    "        # measurement correction\n",
    "        p_t[k] = p_bar * prob_measurement_model(zt, xk, m)\n",
    "        \n",
    "    # normalize p_t\n",
    "    p_t /= np.sum(p_t)\n",
    "    \n",
    "    return p_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONSTANT\n",
    "a = 5.  # size of the environment\n",
    "\n",
    "# feature-based map --> list of beacons coordinate\n",
    "m = np.array([[0., 0., 0.],\n",
    "              [a, 0., 0.],\n",
    "              [a, a, a],\n",
    "              [0., a, a]])  # each row defines 1 beacon\n",
    "num_beacons = m.shape[0]\n",
    "\n",
    "var_v = 1.  # m^2/s^2\n",
    "var_z = 1.  # m^2\n",
    "\n",
    "delta_t = 0.25  # sec\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Discretize parameters\n",
    "delta = 0.5  # size of a voxel\n",
    "num_interval = int(np.floor(a / delta))  # number of discrete interval in 1 dimentsion\n",
    "\n",
    "# list of COM of each cell\n",
    "com_list = []\n",
    "for k in range(num_interval):\n",
    "    for j in range(num_interval):\n",
    "        for i in range(num_interval):\n",
    "            # define the range of each size of the box\n",
    "            _x = np.array([i * delta, (i + 1) * delta])\n",
    "            _y = np.array([j * delta, (j + 1) * delta])\n",
    "            _z = np.array([k * delta, (k + 1) * delta])\n",
    "            # find center of mass of the box\n",
    "            xx, yy, zz = np.meshgrid(_x, _y, _z)\n",
    "            com_x = np.sum(xx) / 8.\n",
    "            com_y = np.sum(yy) / 8.\n",
    "            com_z = np.sum(zz) / 8.\n",
    "            # store coordinate of COM & initialize probability of each box\n",
    "            com_list.append(np.array([com_x, com_y, com_z]).reshape(-1, 1))\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize belief for position tracking & global localization\n",
    "position_tracking = False\n",
    "\n",
    "if position_tracking:\n",
    "    p_init = np.array([0 for i in range(len(com_list))])\n",
    "    p_init[0] = 1.\n",
    "else:\n",
    "    # global localization\n",
    "    p_init = np.array([1. / a**3 for i in range(len(com_list))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t =  0\n",
      "t =  0.25\n",
      "t =  0.5\n",
      "t =  0.75\n",
      "t =  1.0\n",
      "t =  1.25\n",
      "t =  1.5\n",
      "t =  1.75\n",
      "t =  2.0\n",
      "t =  2.25\n",
      "t =  2.5\n",
      "t =  2.75\n",
      "t =  3.0\n",
      "t =  3.25\n",
      "t =  3.5\n",
      "t =  3.75\n",
      "t =  4.0\n",
      "t =  4.25\n",
      "t =  4.5\n",
      "t =  4.75\n"
     ]
    }
   ],
   "source": [
    "# initialize\n",
    "t = 0\n",
    "x_true = np.zeros((3, 1)) \n",
    "p_tm1 = p_init\n",
    "\n",
    "u = np.array([[0.5, 0., 0.5]]).T  # keep constant for the whole trajectory\n",
    "\n",
    "ground_truth = []\n",
    "local_re = []\n",
    "time = []\n",
    "\n",
    "# main loop\n",
    "while t < 5.:\n",
    "    print(\"t = \", t)\n",
    "    # advance simulation 1 step \n",
    "    x_true, z = simulation_step(x_true, u)\n",
    "    \n",
    "    # estimate new location\n",
    "    p_t = grid_localization(p_tm1, u, z, m)\n",
    "    \n",
    "    # store ground truth & estimate\n",
    "    ground_truth.append(x_true.reshape(1, -1))\n",
    "    local_re.append(p_t)\n",
    "    time.append(t)\n",
    "    \n",
    "    # update \n",
    "    t += delta_t\n",
    "    p_tm1 = p_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_final = local_re[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([com[0, 0] for com in com_list])\n",
    "y = np.array([com[1, 0] for com in com_list])\n",
    "z = np.array([com[2, 0] for com in com_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.92, 'Final Belief')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEeCAYAAACt7uMeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXeUFFXah5+uzmmmJ2dmyIJkEBAUMQCCoKi7uophMayuiwEVBRQFQQxrXHTFnFBRRFFRgqBIDpJzGJicQ+ccvj+6unZmGDL4qdRzDmfd6e6q6ur7q/feN11FJBJBRkZGRkbmVBH+vy9ARkZGRubPgWxQZGRkZGROC7JBkZGRkZE5LcgGRUZGRkbmtCAbFBkZGRmZ04JsUGRkZGRkTguqY7wu5xTL/BlQ/H9fQANkTcn8GWhWU/IKRUZGRkbmtCAbFBkZGRmZ04JsUGRkZGRkTguyQZGRkZGROS3IBkVGRkZG5rQgG5RjMHToUD788MMjvn733XczderU4zrWwIEDeeedd07XpTUiLy+PJUuWADB9+nTuuOOO4/pcZWUlAwYMwGw289BDD52Ra5ORASgqKsJkMhEKhU75WH//+995/PHHT8NVHU5DnX7yyScMHjz4uD7n8XgYMWIE8fHx/PWvfz0j1/Z756w0KA0fvsdiwYIF3HrrrQB88MEHXHDBBY1enzlzJpMmTTrla5o8eTJqtRqTyYTJZKJDhw7MnTv3pI41ceLE4zZcb731FsnJydjtdl588cWTOp+MTEPy8vLQ6/XSWDaZTJSVldGiRQucTidKpfKMnv+DDz5AqVRK527VqhVvvPHGSR1r1KhRLF68+Lje++WXX1JZWUltbS1z5sw5qfP90TkrDcrvleuvvx6n04nT6eSVV17hpptuorKy8oyes7CwkI4dO6JQ/J5KNWT+6Hz33XfSWHY6nWRmZv6m5z///POlc8+dO5dHHnmEzZs3n9FzFhYW0q5dO1SqY5X3/Xk56w1KbNXx8MMPk5CQQMuWLVmwYIH0emz5u3v3bu6++27WrFmDyWTCYrEAjZfe9fX1DB8+nJSUFBISEhg+fDglJSUndV1DhgzBbDaTn58v/W3+/Pl069YNi8VCv3792LZtW7OfnTx5MjfddJP0/9euXUu/fv2wWCx07dqVZcuWSdf+4Ycf8vzzz2MymY571SYjczIUFBSgUCgIBoNAVFuTJk2if//+mM1mBg8eTE1NjfT+v/71r6SnpxMfH8+AAQPYuXPnSZ23e/fudOjQgd27d0t/O5ImmtLUK7Fnzx4GDRpEYmIi7du354svvgDgySef5KmnnuLzzz/HZDLx7rvvntS1/tE56w0KwLp162jfvj01NTU88sgj3H777TTdeKxDhw7MnDlTmvlYrdbDjhMOhxk9ejSFhYUUFRWh1+sZM2bMCV9PJBLh+++/x+/307FjRwA2b97MbbfdxptvvkltbS133XUXV155JT6f76jHKi0t5YorruDxxx+nrq6OF154gWuvvZbq6mo++OADRo0axSOPPILT6eSyyy474WuVkTkVPv30U95//32qqqrw+/288MIL0mtDhw5l//79VFVV0aNHD0aNGnVS59iwYQP79u2jV69ewNE1cTRcLheDBg3ixhtvpKqqitmzZ3PPPfewa9cupkyZwsSJEyUvw+23335S1/pHRzYoQG5uLnfeeSdKpZJbb72V8vLyk3I1JSUlce2112IwGDCbzTz22GP88ssvx/35L774AovFgslk4sorr2TixInSSuitt97irrvuok+fPtJ1arVa1q5de9Rjzpo1i2HDhjFs2DAEQWDQoEH06tWLH3744YS/n4zM8TJy5EgsFgsWi4WRI0ce8X2jR4+mXbt26PV6rrvuOrZs2SK9dtttt2E2m9FqtUyePJmtW7dis9mO6/xr167FYrFgNpvp3bs3N998M23btgVOXhPz588nLy+P0aNHo1Kp6N69O9dee+1ZGy9pDtmgAOnp6dJ/GwwGAJxO5wkfx+12c9ddd5Gbm0tcXBwDBgzAarUed1bLddddh9VqxeVykZ+fz0cffcSbb74JRP2zL774oiRSi8VCcXExZWVlRz1mYWEhc+bMafS5lStXUl5efsLfT0bmeJk3bx5WqxWr1cq8efOO+L6m2ovpLhQKMX78eFq3bk1cXBx5eXkAjVxiR6Nv375YrVYcDgcVFRXs3LmTiRMnAievicLCQtatW9foc5988gkVFRXHdU1nA7JBOQGOFbh+8cUX2bt3L+vWrcNut7N8+XKAw9xnx0NeXh5Dhw7lu+++AyAnJ4fHHntMEqnVasXtdnPDDTcc9Tg5OTncfPPNjT7ncrkYP378CV+TjMxvxaeffso333zDkiVLsNlsFBQUACenpbS0NK699tpGWjoZTeTk5HDRRRc1+pzT6TzpDLI/I7JBOQHS0tIoKSnB7/c3+7rD4UCv12OxWKirq2PKlCknfa6SkhIWLlzIueeeC8Cdd97JzJkzWbduHZFIBJfLxffff4/D4TjqcW666Sa+++47Fi1aRCgUwuv1smzZspNOFpCR+S1wOBxotVqSkpJwu93S6uJkqK2t5euvv5a0dLKaGD58OPv27ePjjz8mEAgQCATYsGFDo2D/2Y5sUE6ASy65hHPPPZf09HSSk5MPe/2BBx7A4/GQnJxM3759ufzyy0/o+LEMEZPJxHnnnUf//v158sknAejVqxdvv/02Y8aMISEhgTZt2vDBBx8c85g5OTl88803TJ8+nZSUFHJycvj3v/9NOBw+oWuTkfktueWWW8jNzSUrK4uOHTvSt2/fE/p8LBszVtOVkpLCjBkzgJPXhNlsZvHixcyePZvMzEzS09N59NFHj5kYczahOMYSUt67QebPwO+pyEbWlMyfgWY1dfZW4JwGIpEIfr+fSCSCRqNBEOQFn4zMqSBr6o+NvEI5SSKRCMFgEK/XSzAYRKFQoFar0Wq1KJVKufL898Xv6ceQNXUEYpry+XwEAgFJUxqNBpVKJWvq90WzP4ZsUE6SQCBAMBgkFApJvtfY/yoUCrRarTzD+v3we3oSyZo6AsFgkEAgQDgcllLtm2pKrVaf8V5gMseFbFBOF7GBH2sjEQ6HG82eIpGIJASVSoVWq5VnWP+//J5uvKypZmioqVAoRCgUOkxTsb8plUp0Op2sqf9fZINyOgiFQvj9fhQKBQqFQppRNTewI5GI9K+hS0wQBFkIvy2/p5sta6oJ4XAYn88naSq28j9eTWk0GtnN/NsjG5RTpenAB45qUBrScNWiVCrRaDSo1WrZJfbb8Ht60siaakBzmjqaQWlIQ00JgiC5xGRN/SbIBuVUCIfDUkFjw4F+vAYlRmx2FfuMRqORZ1hnnt/TjZU1JRKJRKQajoZj/3gNSsPjxP4BcnLMb4NsUE6W2MCPRCKHzX5O1KA0PW5shuZ2u8nIyJAD+WeG39NTRdYU/0sPDofDh433EzUoTY8bm/zZ7Xays7NlTZ0Zmv1x5Lt8DBrmxZ/uQRkLMAYCAWpqavB6vdjtdpxOJ4FA4KT6FsnI/N45mjFp+J6TIaapcDgsa+r/Abmw8Sgcz8A/XUvqmBBi2SxOpxNBECSXmBzIl/kzEIlETmlVf6I01JTL5ZKTY84wskE5Asc78GOv2W02VCoVBqPxlM4bC04KgiC52nw+nxTI12g0sghk/pDEChdj7qxjjWOH3Y4gCBhNplM6b8NzxSaJfr9fTo45A8gGpRlOZODv2rmTb+bOpaqigkgkwjnnnsuIkSNJz8g45etouGoJh8O43W48Ho8cyJf5QxIMBqWuEkcbt/n79/PVnDmUl5YSAdq0bcuIq68mOyfnlK+hOU3JyTGnD9ksN4PT6aSiouKYA3/3zp28OWMGHrebrJwcsnJyOHjgAK+99BK1tbWn7XpiKxaVSoUgCPj9fhwOBw6HA6/XK3cOlvnd43K5KC8vP7YxOXCA1195BZvNFtVUdjalJSW89vLLVJ7Gjaya05TT6cRut8uaOgVkg9KEYDCI0+mkurr6qAM/Eokwb+5cLAkJxMXHA9FBmpqWhs/vZ+WyZWfk+mIzLJVKRSQSkYKOtbW1UvKAjMzviWAwiMvlorKy8piz/2/nzsVoMklbXysUCpKSk4lEIvz0449n5Ppimoq1dIlpqq6uTsrulDk+ZJdXA8LhsNT+4VjYbTYqy8vJzM4+bMAlJiWxdfNmrrr2Wupqa/nlp5/YsG4dAL379OGiSy8lITHxlK835veNRCJs27aN9u3bo9Pp5J5HMr8bYpqKxQSPhsfjoeDQoWY1lZSczPYtW7jh5puxWa0s//ln1q1eTTgSoUfPngwcNKjZPYpOlIaa2rVrFy1btsRgMMjJMceJbFBEGlbsHs/gV6qit66594VDIdRqNbW1tbz83HO4HA6SUlIA+OWnn/h1wwbGPvooSUlJp+XaYwNcrVajUCjweDx4PB5UKpUcyJf5fyNWDxJzcx1TU0rlEbUXDodRqdXYrFZefv556uvqSE5JQVAoWL1yJRtFTaWmpZ2Wa4/pJRZTkZNjjg/Z5cX/Mj9iA/9IAyUSiVBw8CDzv/mGn5csITEpiZrq6sPeV1tbS5/+/Vn0/fe4nE4ys7PRarVotVoys7Nxu1wsWbDgjHwXhUKBSqWScvE9Hg82mw23200wGJSX7zK/CQ3rt46lqeLCQn747jsWzp9PemZms7GS6spK+vTrx89LlmCtqyM7JwedTodGqyUjM5NAIMD333xzRr5LzCUmCIIUyJc11Txn/QqlucLF5mZToVCIWe+/z+qVK1EKAhHAJcZa2rRtS0paGqFQiNrqajKzsujdpw+Txo9vdsaUkprKujVruG7UKCKRCHt372bZ0qWYzWbO7dKFXuedd9rSjxt+x9gMS+55JHMmOV5NhcNhvvj0U35eskRyJblcLqoqK/H5fKSlpxMJh6mtqSE5NZUBAwfy9OTJpKSmHnbOlNRUtm7ejN/vR61Ws3/vXhYtWMDypUvpcO659OrdG5PZfMLfo6EhbJrSH0s/blovdjZzVhuUoxUuNh38q5YvZ+Uvv9AiL6+RnzUSiWAym/GK6bzDR46kb79+aHU6wqFQswMsNtMJBoN8+tFHLP/pJyJEYy/btm5l8Q8/8MAjj5yQT7jp4G9Iw1TJSCQiucTkzYtkTjdH0lRzBmXj+vUsWbSIFrm5UrxP0pTJREB8WA8eNoz+F16IyWyOph03o6nY8UOhEF998QVLFi0iGAySkprKti1bWDh/Pg888shpSeePnS92zeFwGK/Xi9frPeu3qzhrDUrDgd/0h29u8C9esIDk1NTDRNIiLw+Xy8WzL72EIAgEAgEO5ucT8Ptp1aYNpcXFh82oaqqr6dSlC9u3bmX1ihVkZmfj8XhITEoiMSmJyvJyvvjkE+65//7T+p2brloCgYCUhCAH8mVOlaMVAzenqR8XLiQxKanRmFMoFLRs2ZLa2lqefPpp1Go1wWCQQwcP4j14kLbt2rF/717S0tMbHau+ro7Wbdty8MABli1ZQlZ2Ng6Hg4TERBISE6mprmbW++/z0IQJJ9R08nje23CCGdvXpWH347NJU2elQYn98LGBfzyDv7amhsysrMOOpdPpqKqsJBAIsH/vXj54++1oiweiufc2m01KfQSoq60lFA5z+fDhfDl7NnHx8YetYlLS0tixfTs2q5V4iwW/38+uHTuw22wkp6TQvkOHwwbp8Q7+ht+x4azQ4/Gwe/du2rRpIwnhbJxhyZwcJ1oFD1BdXU28mHLfELVGg9/vx+vxcOjgQd554w0cNhsoFNH4RX09ESA1NRWFQoG1vh6Px8OVV1/N4oULMZhMh2kqKTmZg/n5VFdVkZqWRiAQYPfOnVjr60lMSuKcjh1RqU7tcdicpnbt2kXbtm3PmkD+WWlQjlWx29zfsnJysNtshwnA5XKRkJhIVWUl/331VeLj48nKzgaicZd9e/ZIBYgAbdu356prriFbPJ5Wqz3sXIIgIIjZWnW1tbz+yis4HI6o0QDSMjIYM3bsac1oUalU1NXVNaoeljcvkjleQqHQMTXVdJKWk5NDaUkJiU2yHb1eLwajEYfDwX9eeAGD0UiWWCUfDofZt2cPToeDYCAAQMtWrbjq2mvJa9UKW319s5qKPew9bjfFRUXMeOklyTBB1ODc++CDzU4aT4aYpurr66XkmLOhy8VZZ1COt/2D3WZj9qxZLP/5ZwKBACmpqZQWF2M0GqWZTCgUorqqihtuvplfli5FUCga9R1SKpW0btuW+ro6npg2Db1ej8FoJBAI4LDbadOuHWtXrTqsJsXv86HRaNBqtTw7dSoqlapR24ma6mpee/llJk+fflqDgLGAI3BY0FEO5MsciYbb9x4tjudyuZj7+ecsW7oUj8dDWno65WVlmM1m1BoNEDUYFeXljPzLX1i3ahXhcJi4uDjpOIIg0KZdO6oqK3n8qacwGo3oDQZCoZCkqaWLF6PT6Rpfo3h9cfHxTJ8yhVAwSHaLFtLrdbW1zHjxRZ567jnUajVw4qv+I33vppr6MyfHnFUGxe124/P50Ov1Rx0oDrud9958EyIRUtPSUCqVVFVWYrNaObBvXzQDSxTPkGHD6NOvH9/Nm4dSrT5sEGo0GoLBID6fD4PRyJzPPuPnJUsI+P0oBIG62lpUDQZVMBCgvKyMa667jt07d+J2uchpMPABklNSKC4qYv++fbQ/5xwgaoRKi4sxmEynpcBLDuTLHA8+nw+Xy4XBYDjqeHC7XHz0zjuEAgFS0tIwm81UVVZit9s5cOAAJpNJ0s7Fl13GRZdcwvTJk6Ux2PDYsQmdy+kk3mJh3ty5LFm0CL/Xi6BUUltTE3XZipoKBYOUlZZy+RVXcCg/H5vVepimEpOSKC4uZvfOnXTp1g2I7nVUWlKCyWQiOSXltBiXhoH8hvVif5ZA/lljUMLhMNXV1TgcDtq2bXvU9/7y00/U19XRpWtX6W8ZmZloNBqSkpO58pprIBKhVZs2FBUW8u/p09m1bRt2hwOLxULbc84hUVx1hMNhIpEIWq2W1195hV07dpCWno5Go5FcWofy86VMMYVCwfCrrmLQ5Zcz78svpdlSUxREZ1XhcJiF33/PR++9FxVlOEzb9u259Y47yMjMPOX7dqxAvpwqefYS23OkurqaDh06HPW9q1asoKqykm7du0t/S8/IQKfTodfrue7GGwmHw7Rs3Zqqykpefu45Nm/cSF1NDZaEBNq0a0eyWBwca+yoNxh46/XX2bh+PekZGWiSk/F6vdTV1VFw6BAGoxGf14tCoeCyIUMYPnIkSxYtQjhSTYyYohyJRFi6eDHvvf02enGlk9eyJX+/4w5ycnNPy71rGsiPeU3+6IH8s8KgxKrgj/fBt2blSuLFXkINSUxKoqS4mPbnnIM5Lo6flizhpWefRalUkpicjNPpJBwOs23zZnr06kVcfDyVlZV07tqVyooKdu/YQXZOjvSA1uv1nNOxI0WFhVxz/fV06NiR7OxsyW2WnJpKMBg84nVaLBbmf/MNX33+OQajkcysLCKRCEWFhTw7dSpPPfNMs9/jZGkadJRTJc9eYlXwx6up1cuXY27guooRb7FQWlJCi7w8UtPSWLV8Oc9OnYoCSEhIwGa1Eo5E2L51K9169iQhIYHqqiratG2Lw+Fg88aN5OTmSuNOp9NxTocOFBYUMHzkSLp2705mZqZ07qTkZMJHKERUKBQkJCSwdPFiPn7/ffR6PRkZGSgUCirKy3lu2jQmP/PMafEANDxn00B+w1XLHy055k9vUJpWwR9PF9HQ0d4j5rrPmzuX56dNIxgMRjO9/H7cbnf0+AoFu3bsICsnh9TUVG64+WaWLFzY7AM3NpiCwSDtzzmHivJyvv36a/bs3InOYMDldOJwODA3KMqqq60lMTmZ7JwcXn3hBTKysqirqwOiAzQ5JYXSkhJWr1jB0BEjjvs+nQgNXWINNy9SKBQYjUa559GfmKZV8MczdsJiQklTGq5+F37/PdOeeAKv14ter6eqqgqXy0VIrOfauX07uXl5JCQmcsvtt7Nh7dpm4zaxsefz+Wh/zjnUVFez8LPP2LFtGzqdDq/Hg9VqlRpQAtisVuLi4mjTvj3vzJxJeno6Nrtdusak5GTKSkr4ZelSrr3++pO/eUchFshv2Fo/9n30ev0fYsL2pzYoTSt2j9ajKxwOU1RQQCAYpFvPnnwzdy40Wd7arFZycnOpqanhi08+iQ60pKToMY1GVBoNSoWCnLw86mpqokvkvDyWLFzInM8/p6iggFyrldzc3EaV8Aqig2bv7t38e/p0QqEQcXFx+CoqcDqd7Ni6lewWLaR2KvHx8fzrgQeoqqwkJPYNa4rJbGbbli0MHTECl9PJ0h9/ZNmSJfj9frr16MGwK69slNFysgHIpi6xDRs20LNnT3nzoj8pzWnqSJO0cDhMcWEh/kCAbj17smP79sPe47DbSU5OxufzMev99wmFQv/rcaeI7lMSCoVo3bYtVZWVjLr1Vlq3bcuKZcuY+/nnHNi3D5vVSm5eXuONuCIRBKWSQ/n5PDt1Kn6fj7i4OKlNfVVVFTktWqBSqQiHw5hMJu598EGs9fX4fL5o5ploUGLEWSxs27KFa6+/Ho/Hw88//shPS5bgcbvp3K0bV4wYcVpcYs1pqkePHn+IQP6f1qA0V7F7pNnU9q1beev116mrq0NBdIXidDgoLysjNS0NQRCor6vD5XLxz/vuY+WyZVJg2ulyQSSCRqPBoNfjcDjIysqiZatWtGnfnulPPIHNZiM1LY2KsjJqqqqoq6mhW8+eGI3GqNtAqSSnRQtmzpiBXq+XslpMJhMJiYkUFhRw8WWXERcfT1p6Op27dkWr1VJcVCTFaJoSDAQwGI24XS6eeeopCgsKSEpKQqvVsnrFCtatXs3EyZNp2bq1dL9OlYatKeTNi/58NFcMfKTfc8/u3bw5YwZVYsv6cDiMy+mkrKSEtPR0BKUSm9WK3WbjgXHjWLNqFYI4CXG5XEA0oUWn1+Ow20lLTycjM5POXbsyfcoUqquqSExORldcTG1NDXU1NXTt2ROzWE0vCAJ5rVrx1n//i1IQGk2eLAkJFBYUMGDgQBKTk0lJTaVz167o9Xqqq6qOqimj0YjX6+XfTz/N/r17SUxKQq/Xs2HtWtavWcOjkyZJiTKng5imYt6A33tyzJ/SoBypYrc5g1Jw8CDPTZ2K0WyWBp3X7aayvJy4uDgqyssJh8O0aduWv954I3qdjjUrV1JVVYVCEPC5XFgsFvyBAD6XC7/fT0lxMVddey0P/POf7N21C51eT0SM4wQ1GtQqFQfz88nOzsZhtzPyr3/FWl+Ptb7+sDx4QRAwGo24XC5uuPlmAKqrqti5fTuhUAiTyYTNaj3s+7tdLi686CJ++flnCg8dapTVkp6RQW1tLR+99x5PTJt2zIfDiXKknkdyIP+Py5GKgZtzI5eWlPDM5MlodToyxZosv89HeXk55rg4KsWVdV7Lltw1ZgwJCQm8/847VJaXo1SporVdCQkEAgFcTid+n4+S4mKuuPJKHr73XrZs3oxerycC+MQYnkajIX///mgtitXK8KuuwufxUFFWdlhyiiAIxMXFUVdXx8233QZEG7puWLsWv99PYlISdeIGeQ1XCna7nQEXX8za1avZt3cvOQ3ioWlpadisVt5/802eeeml0/6Q/6N0ufjTGZSjVew2Z1C+mzcPQalslOuu1elITk3F6/Xy+jvvANFg35ezZ7Ns6VKqqqooLipCqVQSCgZRKpVRH6dajd9mw5KYyPxvv2XPzp0kpaRIP7Tb6cTn92MymykvKaFX797cdtddeL1evpw9m0MHDxIRU5UburHUajUOu51IJMKXs2fzzdy50e8RieB2u3E6nZjj4zGJKx6H3U7f/v3p3K0bsz/5pNm9VxITEzl44AD1dXUkJiXhdrnYtnkze3bsICk5md59+x5WcHYyyIH8Pz5NM5GOpalF339PKBxulBCi0WpJTU/H6XTynzffRBAEdDod382bx+IffqC8rIyy0lIEpZKgqCmDwYBarcZus2E0m/l56VK2btlCYmIiKlEfbqUSr9cbTUOuqKB7z57cPHo0giDw+SefcCg/n3A4TGpaGhqx1gVETYnFwvO/+YYvPvkkahgjETxeL3abDXN8PAa9nmAwiN1up3uPHvTq04fpU6YQHxd32LiNi4+nrKyMivJyMjIz8Xg87Ni6lX27dpGQmEjvvn2lTLVT4fesqT+dQTla4WJzg3/n9u1YEhJo8kZ0en20RbXLRbzFwszXXuPdmTNRKZXojUaIRCSfbGxTK5vNxtARI/jbTTfx9JNPotFq8bhcqDQaNGo1BpOJoM1Guw4dUCmVPDR+PLM//piVy5cTDAbxejwc2LePkuJiunTrhl6vB8DldtOlWzfWrVnDV198QUZWlpSLHw6HOXjgAAmJiZjMZkwmEwMvu4wevXqhVCoJBgIoj9BMT6FQEAqFKC4sZNqTT1JWWoolIYFQKMSnH33Evx54gD7nn3/afpsjBfLlzYt+3xytCr45Te3Ytq3ZlioajSYaEK+vJz0jgw/ffZfXX3kFQRAwGAwAmE0mgqFQtLBXp8NmtTLwssv4xz33MOWxx9DpdHjcbtTimDEYjQSDQVq1aUOHc8/l4QkTmD9vHj8vXRotIvT7ObB/PyVFRXTu3h2jeB6Hw8Ggbt3Yunkzn374IemZmY0KGmOp/EaTCbPZzICLL6ZXnz6oVCoCfn+zq4HY/QmKdS/PTJnCoYMHSUhMJBwK8dlHH3HnPfcw4OKLT8vvEjtnc5pSq9VotdrfXFN/KoNyPC1Vmg5+k9mM0+k8rF1DOBSCSAS1RsPLzz/P52IQXq3RUFdTg6BU4vF4pPjKOR07Muyqq7jp73/nu6+/xuN2k5qWhrWuDpU4i4KoASgtLmbYiBEsmD+f9956S8oOMxoMUnrzgf376dS5MzXV1VgsFvqcfz7PTJlCnMXSqOeQIAhk5+RQUV7OEx98IA10n89HfV0dXXv0YOmiRehFIcVwipt+WRISGPfkkwT8flJSU6WeYx6Ph9defpnWbdqclllV09+h4fJd3rzo98uxquCUQUvyAAAgAElEQVSb05Q5Lg5HaelhWzDEspd0Oh0zZ8zgw3ffJRIOo9HpovFLhQKvWJxotVpJz8jgsiFDGH3nnfy4cCFuUVM11dWo1Gq8Ph+IhbclRUUMGjaMVStWMPP113E7nQRFl7DX60WpVLJ/zx66dO9OXW0tRpOJCy+6iP+++ipGk6mRR0ChUJDdogVFBQVMnDxZmtj5fD6s9fV06daN+fPmHdYO3+12YzKZSM/I4PFHHsHpdJKcmkqKqB+f18tbr79Om3btTluLl4bX3FBTMTfzb50c86cxKKFQ6LjaP0QiESorKti5fTvhcJge553HvDlzMJlMjfzC9XV1XDZkCIfy81m3ejU6nU5KRYzNOHw+H/0GDKCirIzHpkxBUCqlWUlhQQEarRa/308coDcYiEQi0sY8OoOB56ZOJRwKYTKbCfj9+AMB/D4fSpWKspKS6P4onTtz0+jRHMzPZ/u2baSmpR2WkaXT63F7PNLM7fNPPmHJggUEQyHCoRA2mw1BEEhNS0OhUOB0OLBardw/bhz79+6luqqKtIwMrA1iMXq9nvraWtasWsWIkSOB6EAtLSkh4PdLm4adKg1nWLFAvkcs8CwuLqZTp06nfA6Zk6PhltjH0lRtTQ3bNm8mGArRpVs39uzcSbzF0uhz1vp6+p5/PjU1NSxbujTaMkXsIhFL4XW7XPS78EIqKyp4eMIE4uLi+Pf06RzKz6ewoACtRoPP74dIBIPRSCQSwWGz4XK7SUxKiqYdezyY4+Lw+3wERE2plEoqKyooOHiQczp25Jbbb6ekuJgtmzZhMpuJhMON2uLHOlw4HQ6USiVfffEFC777Dn8gQDgUwi5mgGVkZiIIAk6nk7q6Ou6+916KCwspKSoiPSNDSueHqCsdYMWyZVw/ahQQ1VRZaSk+r5eMrCzJeJ0KTTUVC+QrlUry8/PpJnYCOBP8KQxK061Gj8YP337Lr2vXNsrkUCgUFBUWYjabUSqVOBwOTGYzN/3973z1xRdoNBpMZjMV5eXEi92BBUHA4/Hg9/lIz8xEq9Mx4cEH8fn9xMfFYRB7fkUiEcJirCMYDJKYmMhzr7zCO2+8QcDvR6VWR/dNUSikWpO8li0xmEyMHTcOnU7Hay+/jMvppKaqinLRLdWxUydpgPp8vuiOkDodLz/3HL+uX09aWhpqjYaA34/H45Ga1EWIBuX//o9/0PO881i9cuX/DGmTe6VWq6Xd8/bu2cPM//yH8rIyFIKAVqPh+lGjuHz48NOymmgayC8pKWHcuHEsOEM7W8ocnYZbYh/r9126aJHUdyumKUEQKCoowGQ2o1KrcdrtaLVabrvrLn768UeUSiVms5nioiLi4+P/FxMg2hwyOTkZi8XCxHHjpBYrJpMJBUia8ng8BAMB4iwWnnnhBebOmYPX40FQKqMxVEHAJK7MM7OzSUhM5J777iMxKYmZM2Zgt9moKC/Hk59PvMXCuZ07oxMf6IFAAEGpxGQ28+Zrr7Fi2TIpDhMIBPB7vVjr66M3QKz9unfsWPr068emDRv+52pqcu+0Oh0V5eUAHDxwgDdmzKC4sBBBEFCpVFx93XVcdc01p2U10XTVYrVaGTNmDCtXrjzlYx+JP7xBCYfD7Nu3j6ysrEZBt+ZYs3IlPy5YQLv27RvFIIqKiuh34YUEAwF8fj/n9emDQqlk88aNfDN3LiXFxdKGWQ2JRCJUV1VxxVVXce+dd0ZnUDodRZEIIXEjILVKhcftpm379qjUau657z6ysrIoLy0lJzeXyooKabMtRMNQXFTEBQMGkJ6ZyfixY6mtriYCpKSnU1ZSgtvlYvfOnXTr0YMIUFVRQd9+/SgsKGDTr7+SlZ0tDSS1RkOrNm0oKy3lwQkTaNGiRaOZY2zFE2mmliAQCJCbm0tJcTFTH38ctVpNulg57PN6effNNxGUSoYMGybdj9NBzA99OlZAMidOJBIhPz+flJSUY86YN2/cyMLvvqNt+/aS2ygcDlNSVETP3r1Rq1S43G56jhyJUq1m965dzPnsMw4eOIDeYDi8hiUSobqykmEjRjD2X/9i/969aHU6iouKCAWDUm2KpCmVitvvvpsOnTpxYNo0clu2pLSkREok8Pl86LRaykpKyGvVityWLXn8kUeoKC8nEomQkppKcVERXo+Hndu306NXL1AoqKyooGfv3tRUVbHyl1/IzMqSHvJqtZq81q0pLyvj3oceok3bto22oUhJSyMcDjdbzOn1eslr2ZKqykqmPPYYgKQpv8/HJx98AMDVf/nLqf2ITYhp6ljPyFPlD21QYr7Curo60ptsuNMc3371FSaT6bAYRFpqKru2b+edWbMQBAG/388D99yD0+EgOTWVwoICKWXSbrejFlcVPp+Py6+4ghXLllFWWopWq0Wv10eD4cEgbreb1u3aUVNVxcWXXcbQESPYvnUrT4wfz56dOzGYTPi9XgwmE2pxH3i3243X4+G888/njVdfZa3oblMKAsFQCK/HQ1inw+VycWD/fvR6Pb379qXn+eezYc0ayUXRkJhboriggC5du+L1elm9YgUrli0jHA6jUqmoKC/H0KAwzG6zodPr6du/P5/NmkUoFGq0UZhWpyM5JYU5n33GpYMHSwVip8tP6/f7z/jglzmcmKbq6+tJaJqs0gzffvUVejEbK4YgCKRlZLBr+3be/+wzaZOsh+67j7rqahITEzkQDuP3+YiEwzjsdtQaDRFx58OLBw1i88aNFBUUoNZoJE2FgkFcbjet27ShprqaCy66iKuuuYY9u3fz+COPsGf3boxi/y6lUin983i9uBwOuvfqxYfvvcfKX35Bp9NJGWU+rzdaJ+NysX/fPoxGI126dqXfwIH8un69VNfSkNjsP3//fnr17o3f72ftqlX88tNP+AMBdDodZSUljWKXTocDjVrNBQMHsmj+fHw+X6OUZo1WS1paGvPmzGHo8OGHdUw+VXxiF/MzyR/WoDSt2D2e2XFpkx84ht5goKy0VFpCT3viCRbNn4/JbI66t8TluFHcuCcnNxdrXR3XjRqFoFSybvVqKf7hcjoxmkzSZ5WCQLv27bnymmv4es4cVq1YQUpKCmmZmVjr6wkTHWgqlSoacxGNVFx8PPPmzkWtVmMwGqWGdiqVCpVaTVJSEhddcglDR4wgf/9+3poxA6fDQf7+/Xg9Hlq2bn3Y4FFrNDgdDiY/9hgHDxzAKHZNtlmt1NXWEm+x4HW7UQDm+HjGP/EE5rg4tm3a1GxPML1eT0V5OdVVVWRkZlJSXMy8OXN45/XXMcfFcfkVV3DhwIFHbHB5NH6LwS/TmKbFwMejqZKiInTNaEqr1VJXW4vDbkej1fLc1KnM//prKVYpiEWuJpOJCJDXqhV1tbXcMnIk6RkZvPnaawQDATweD26XC4PRSFxcHIIgEAqFaCtq6seFC/l56VKSk5PJycmhuqoKiHYiVqpU0fovr5eBl15KTm4u/3nhhWiav8HQaMUBkJqdTf8BAxgxciSlJSW8/frreJxO9u/di9vlomWbNugarJrDkUg0k9PjYdqkSezetQuDeFybzUZtTU2044XXG22fYjAw7rHHSEpKYsumTc32NtNotQTq66koKyOvVSvKy8qY//XXvD9zJgaDgcFDh3LRpZee1Oo95ho/k/whDUrTit2jtX9oSFp6OhViTKAhXq8Xo9GIQqHgsXHjWLViBVq9Hq1eH10RhEIYzGZ8Ph8Ot5tWrVvz4PjxGAwGnn7ySfx+P3q9PppOqFbjdDqjsyNBoFLMjf91wwbeFVvi7wEp/VCj1RIKhcjJzSUUCpHWuTOPTprEl59/jsViwVpfj9/nA/EzOnF1Em+xMPyqq9i6eTPffv01gUCArOxsSouLpR5IXbt3l1ZLgiDQrUcPvv7ySw4eOCBtAgbRJpMKhYI27dpx5dVXEx8fz7ldukhCM5pM1NbUQBP3RzgclrJ29uzaxZMTJmCz2cjMysJZWsp/XnyRVcuXM/6JJ07YqPj9ftnl9RvStBj4RDS1d8+ew/4e8Pujqas6HU+MH88vS5ei0WrR6fV4xKxGnZiM4ha7Tdz74IOkZWQwZcIE/D4fBoMBn9+PSqXC7XKhVCpRqVTUVFXRpVs3Duzdy8wZM6SaM6VSSSAQkDQVc/0mJCYyaepUflywgPj4eOpqa6OTUUAd05TTiSkujuFXXcXB/Hy++OQT/F4vGZmZFBUWUltTg8vloluPHqhUqug5gV69e/PDt9+ye9cuMrOyJO9AvMWCUhDIyM7muhtuwGQy0UnscAFRTcWMX9PfIRQOo9XpOHjgAJMefZTq6mqysrNxOhz89z//YfnPPzNp2rQT1sdvoak/nEFpOvBj/5qbTYVCIbZs2sSmX39Fq9XSs3dvPvv4Y8IN/KGxOMiNt9zC+jVrpOB8IBAg4PdjMpmk7KPLr7iCiooKJj31FNXV1Yy7/35cTqcUnA+Hw/jEFZPNZkOlUpHXujV9+/dn3P334/f5iLdYJLdarIdQbF+I62+8kUsGDWLt6tUsmD+fgN8PRAe9IIol4PdLBtCSkMDbb7xBVWVl1EUkuhYgGv+orKhAr9fjdDq57sYbSUlNZdEPP0hpjA1JSU3lwL59DLj4YkkUxYWFlJWV0bFTJ777+mtMZnMjd1ptTQ3ndu6MJSGBxx95BKVKJbWi0Ov1mOPi2LRhA2tXreLCgQNP6HeWVyi/Hc0VAx9JU+FwmO1bt/Lr+vUolUq69+rFpo0bCYVCjYrtKisrufKaa9ixdSv79+7FHBeH1+vFLxb2xro5DBk2jOqqKh6eOJGA38+DY8Zgt1qjmvL7o65lccVkt9lQazRkZGRw6eDBPDp2LB6PB0tCAoIgEPD7CYXDGI1GqquqsNtsXDdqFIOGDGHLpk18N28eHo8HiE7OYgbII2YWqtVqsrKzefSBBygtLcXv83FAqyUUDEafO36/tCGYw+FgxMiR5LRowdRJk0hMTDzM1Zycmsqh/Hz69u8vPchLS0ooKS6mbfv2bN+69bBMuLraWlq2akVaejqPjh1LKBwmKTlZqtMxmc3s2L6dX5YuZbAYuzxe/KKRP5P8oQzKkargmxv8TqeTyRMmsHvnTmn5HrP85WVl0vsUCgXnX3ABI//yF6ZPnoxCoZDazseJD9BYCl5sD2y/38+T48dTWVaG0WSKrk4CAdQqFUQimOPicLpcZGVn88BDD/H0lCnU1dTgDwSi7VLMZnQ6nWQUe/fty9Arr6TXeefx4rPP4vV6iYTDUm5+bAWkUCgIhcMoBYHLhg5l+lNPUXDwIIlJSdHKY60Wn9eLz+/HaDBQU1PDBQMGMOjyy3G5XEydNIndO3aQ27IlaWlpUrUxRLse+3y+aOzH5eLFZ59l25YtkouhXmyal5ySglKlwm63ExcXx+13301xYSEV5eWkpKbicDga3Vu90cjSxYslgxIMBtm+ZQu1NTWkpqfTqUuXZuMu8grlt6O5+q3mNBVz7WzZtCmqKaL1WhqNRspcitG9Z09uuPlmZrz0EuFIhHiLhbKyMsxihblCoYBIhJrqagwGAzqtlnH33UdZSQkGg0GKhag1GmnXRqfLRVp6OmPHj+eFZ5+VJlKhUAiz2YxOr8coFvid16cPlwwZwsBLLuGFZ57B6XAQCASw1tcjCAK++vqo20vUlCAIDL7iCp5/5hn279tHQmJi1E2l0+Hz+fB4vRhNJmqqq2nfoQODLr+ccDjMtCeeYPPGjWRmZZHRoDgSkJq5+n0+goEALz//PBvXr0chCNG9V+rq8Hg8pKSmSt0wdHo9d48ZQ011NQcPHIjWsjVI51coFJjj4vhx4ULJoIRCIXZs20Z1ZSXJKSl07tat2cJLeYXShCNV7Da3PP/wnXfYvXMnGZmZ0nuDoRD79u7lltGj0YgDtWuPHlgsFma89BILv/+e+rq6qFjCYTxer1R/EgwGqaur47LBg7l79GhKS0oIBgJ4a2ulzqaxvH19KIQlPp5xjz3GF599RuGhQ1gSEqgV+wPZ6utRJCZKfubcli1pkZfHs1OncvDAAQJiY0e1Wh1N/Q0ESBfz3d0uF5ddfjlDr7iCd//7X9RqtRRLihCtSQmJArxp9GhGXH01z02bRnlpKXEWCwajkQP79lFVUUGnLl2krVcdDgfZYvfVZ6dOZc+uXaSnp0sPFo1ajcfrJSsnh2AwyOVXXMElgwaRmJTEvr17jxiMV4n7eEO0b9pTjz8u3QcFkJGVxaSpUw8r9Pot/L0yRy4Gbk5Ts2fNkh6esfeGxD3eb7j5ZuLi4wkGAnTu2pXUtDTeev11vv3qKyorKqRJmdfjQafXSxOVutpa/nrDDdw9ejQFhw4RCgaj40V0u8U0HwgEMJtM0Ur4r78mf98+4i0W6mprUYhxQIgmizgdDkLhMLktW/LiM8+wa/v2qC4NBqmhYiAYJC09HZWYhdm3f3/+ct11XDdrFmq1mkAgIKUna7VagsEgBpOJv/7tb4y69Vb+PX06BQcPSunMBw8coLqqik5dukjj1uV0kpScjMls5uknn+TX9eslTUG01sXhcJCdk0MwEODSwYO5bMiQ6I6shYXR34TDe+ypxKJqgJLiYp56/HGqKiqIENVUckoKT0ybRou8vEaf83q9skGJcbSK3aazKY/Hw48LF0qFfDEEQcBkNLJm5Ur+8+abQPRBOu7++7HV19PunHNYu2oVOnFQxircQ8EgarWa+x9+mJefe47Kioro3zQa/GItSSAQICsnB7fLRXZODnfecw9ffvYZm379FafTGfUTA4FwGEGpxOlwYDSZ8Pv9DB46lJ8WL2b5zz9He/Go1Vjr66VBHQwGsdbVkZKWxmVDhnDbnXfy85IlhEIhdDpdtIeYWBlLJIJCEKitraVX37788O237Ny2Dbvd3mhAuVwuiouLadW6NV6PB6fDwbXXX0/+gQPs2bmTtAYDXyHm2VeUl3PJoEFcMmhQo/vfIjdXMmxN0ySdLhc9e/fG4/HwxPjx+Hw+0jMypNerKyuZPHEib7z3XqNZlbxCOfMcrRi4qaZCoRDff/MNKampjd6rFARMZjMrli3j3VmzgKj+HhW3V2jbti2VFRVoxTiFoFTi83qluN6YsWN59803KSspkdJawz4fapUKv1hA63G7yczK4u//+AeLFyxg9fLlOBwO7HY7CsAvZio6nc7o6sPn45LBg/l13ToW//ADarF5YkxToXCYcChEfW0tGVlZXHDRRfxjzBhWLFuGx+2WAvbBQEDSlKBUUl1ZSd/+/flx0SI2/forLqeTvXv2oNVqUSgUuN1uigoKaNu+PT6fD5vNxrV/+xulJSXRXSUbaAqiG/b5AgH6nH8+I66+utH9T8/MxGg04hE7bDTEYbdz0SWXEAgEmDxhAjarlbQGmqqvq+PJCROY+cEHjTQkr1BEjlWx23TwOx0OKR226fs0Oh1VlZUAFBUUMO6BB/h13TrJBaXX6yUj4fP5aN22LT6fj4cnTGDd6tXs3b07GiQMh4kEAtHcePEa7VYrWp2OSwcPZt+ePaxbs4b4hASSwmHq6+sRFArCkQhK0VBpNBqGDh9OJBLh43ffxefzoVKr0QoCGrFHkd/vp3OXLuS2asWYsWPZsW0b4x98EKfTGW1QKQiEIxFUKhUms5lgIIDb6SS7RQtQKHjz9delPbaVKlW0wFK8l0UFBRiNRnR6PbfddRcKQeCHb7+VNvZpilKpZN+ePVwyaBA+n48F8+fz/bx52O32aP1NQYHUUDISDlNbW4vZZOKyIUNYv2YNNputkTEBSBIN1dZNm+hx3nnS3+W04TPLsYqBm5ukeTyeZlOJtVotNWKAuay0lAkPPSSl5obCYXRiwopa1FTbdu3web3c88AD7Nuzhy0bN0pp536/P6opcXVgs1rRGwxcOHAgleXlLP/pJ0xmM0nJydTW1EiaihkSr9fLRZdcQoLFwr+nTYu2XVGpELRaDEYjITH1vlPXrqSlpfHg+PHk79/P4+PGRSdZYtPX2PPDJPYWc4klBCqNhpn/+Q8lxcWoxdWOx+2OJgMoFJQUF2M2m9HqdNxy++0ISiXz5807oqa0ajV7du1ixNVXEwgEWLxgAd9+9RXW+nr0Oh3FhYVYxOaukUiEutpadHo9w668ks0bN1JdXX2YphISE6koL2fj+vX0u/BC6e+/haZ+9wbleCp2my7PLQkJaLXaZt0mbrebTp07U11VxUP33su+PXuiGV7ijCQ2g45lgmRlZ/OPf/2L4qIivv7iC9xuNwaDIZoJIxZZQVRUSqWSSwYNomXr1kwYOxan04nNapXSjsNi36FY3n7nbt1ISk5m9qxZ0SW+QoHX48EnBhpVajU+rxeXy8VlQ4ZQVlrKh++8I+2ZkpycjMPpJBgIRPP4IxF0ej0ms5lJU6dSUVoqDfBYEFIlnjscDpPXsiWTp09HrVbz4rPPUlNdjd/vp6ioCLvdzjkdOjTKhQ+GQiQmJ0szo61btmCxWNDp9Vjr63G5XET4X8+mjueeyz/GjCExKYnioqIj/8ahEOVNfPCyy+vMcTydJZpqymAwYLFYpPHfEI/bTfsOHbBZrTw0Zgx7xPRZpVIJYp1HWkYGtgZxuLvvvRer1cqnH374v1WBeD0arVYay4Ig0O+CC+jVpw/33303ToeD+ro6qU2KpCm9HrVaTcdOncjNy+Oj996LlhSIkzef10u8xSK5eG02GzfcfDPW+nreeeMNNFpttA9Xejr19fWEQiFUajURoto2GI2MnzQJh83Gwfz8qIYbakrsStG2fXsmTplCXHw8r/7735SUlBAJhSguKsJmtdK+Y0cMDbIl/YEAyamphEIhpk+eHJ2ExsdLzWljGXEK8Xdr064dd48ZQ2paGmtWrjys2DpGJBKhpLi40d/O+hVK0+17j4RCocDhcLB21Sp27dhBaloaAy6+mB++/bZRhavf78fv9fKXv/2N77/9FofdjslsjgbqiNajxGIl3Xv1AuDhCRPYtXMn06dMwePxRDO+XK5omwkxlz5Wwdupa1eGXXklD40Zg9fnQ9+g2WMgEMAcF4fH7cbn9TJk2DD69u/PR+++S0lxMW6XC62Y7qgQ27/E9oRQq9X07tuXf44ezY6tW/8nJrHVRSxw36ZNGzxeLwMGDqTfBRfw31dfJT4+XnrAB4JBlGKLB4fbTXZODnmtWjHuvvvwuFwoBIHExEQMBgNup5M9u3fTtVs36fgKhYILL7qIVcuXs23r1kbxqZTUVDQaDf5AgBdnzMBgMjXae7thUWRTlErlYTNf2eV1Zmhav3UkYi6cRT/8wPYtW0gU656+/vJLafIESAW8140axY+LFlFXV0dcfLyUEqvX6wlpNNTV1tK9Z08EQeChCRMoKynhqccew+F0EhKzKMPhMII4nkPBIAqgU5cuXDdqFA+NGYNHjL/EGokG/H5MYp1HbGUy5IoreHfmTMpKS6Oa0ukIBoOoRE0lJiVJx+5/4YWMHzuWrZs3S/eikaa8Xtq2a4fH44kG+QcN4pMPPiAuPp5QMEg4EiHg86EUBJQqFcFgkHiLhY6dOvHo2LFYrdaom13sAu5xu9mzaxfdevRAUCgkl/bFl17Kxg0bWL92bSNNJSUno9NqsVqtPPfKK5jN5kY6iiUOHOn3S2qgPzjL61COd+BDNBXvpWeeiVbIqlSSGyqvVSvKSkulASIIAsOvuYbuPXvy0rPPRgurzGYqKyqk5nSxJWxtTQ1t27XD4XDw7JQp1FZXS/ueeL1eaZaflJyMw26nRV4ed48Zw8SHH8ZmtUpL95C4x4IgCHg9HuLj4+nUpQu33XUX/7rjDrweD1qtlghI1fgQ3R3O6XSiVqu59Y47+Pj991m9ahUmk0lKFAiFQrhF/3JFRQWdunblksGDsdtsPDRmDOvWrMEnpl4ajUZ0osGKpTl36taNX9evZ/fOnVRVVkrL/KCYJumvr5eW9kB0M6TERH5cuFDKOmuIwWjEVlqKx+uVAoKVFRVs3bw5GkQUU0UbdqF1iLGkhu4uiBqUWG8zmdPDiWiqsryc56ZOxeV0omqQjp7XsmU0SzISAXGiN2T4cPpdcAH/HD2aiNi00e/3S8WwSqUSv89HfW1tdFxEItFAclUVCsBoNOJ2u1Gr1YRCIRKTknA5HKRnZXH/uHFMnjiR2poaKaMrFAoREV3HHrcbS2IirVu35t4HH+S+u+/GZrWiEVcXAfH7hoBwIIDT4UBQKrll9Gi+/eorli5ejMFoRKvTRTO+RE1lZGZSVVVFu3POYfCwYXjcbsaPHcv6tWvxuN3R7ynuPxIWK/x9Xi+du3Rhy6ZN7Ni6NRprDYUaa8pmo7iwUNp07+933EFGVhafzZrVbJdtrV5PoKYGh91OK3F31ZrqarZs3IhH7MzsFHsPxnCJk9OmW0+ctSuUSCRCaWkpQLM1Ew0Jh8O89uKL+LxeUhu0X/H7/RQcOsRTzzyDy+VCpVLRrUcPFvzwA/f+4x9s37pVCpRrtFrcLhdqMbjm8/nw+f3RYN0tt1BRUYEgCDjsdgRBQC12Ig2FQricTsxmM49NmcIH77xDVUUFRjH+IYjN4RSCEBWV309ScjLjn3iC999+W3qIq9XqaLxHDKjHBkdiUhLZLVrw05IlrF6+nIjYHsLn9RInNtTTarU47HY6durExMmTee3ll1mzahVJycnktWrF1k2biG0dqlKpor2QtFoSEhPp1rMnb732GsWFhZji4lCA1FbG5XYTFx9Pi7w8evftS8s2bVj+00989O677NuzB5V47kadB8SHTCxv/+3//pev58yRXnY6nVRXV5OckiIVh+n1ep6YNu2wNhM+n6/RCkfm1IhEIlRUVOD3+0lLSzvme//76qvY7fZG2XeBQICCgwd58umnpRVr1+7d+WnpUh4aM4YN69fjsNmi2YY6HW6XK7rPjTj2PR4Plw4ezJ233EJJURFKcfKmEAQ0ajWhYDAar9+PSiUAACAASURBVHA60RsMTHrqKT5+7z3KS0sxGAyERPeOAkChiCaj+HxYxK4Ocz//nLKSEilhRi02Z0WhiHabEAQSEhPJzMpi7erVrFu9Wqox8/l8xMXHo1Iq0el0WOvraduuHZOmTuX9t9/m5x9/JDEpKVrRX1MTbWLZQFNqtZqMrCz6XHABs2fNouDQIeLi4lCp1WjEyafL5cIcF0dWTg59+/WjXfv2rFqxgi8++4wD+/cTFvXQqO1/gxVbJBLh4/ffZ/bHH0tZnR63m4qyMlLS0qKaCgbRaLU8NnnyYe31z8oYSqxw0ePxHFfrh907d1JTU0NqE5eKRowpbNm0ibvvvReIBgzfe+MNUtPTadOundS6OuZfhWiqX4vcXEbfeScvP/88lVVV0fbX4n4CMfdVTm4uDpuNrJwc7vzXv/j4vfdYv2YNLrcbp7jJTYioLzoUDEoPzDv++U/27tnD3NmziUC0fbfYS0ij0RAS283Hx8dzXt++1NfW4nG7sdvtGMT9rMPhMA6Hg3iLBZVaTV1tLdfecAP79+5l6eLFuFwu8vfvR6vTRfP4xc6r53TsGG33bbcz7Mor6dWrF5OLi6O+YrGZXazVjFasafnHv/6FwWiMNofUaMjMzsbv97Nj2zZ27djRqENrrKq5ZevWLJg/ny9nzyY1LU1yjyQlJ1NWWkqv884jLSODFrm59B/wf+ydd5xU5fX/33d625nZne1so4MCgiJYEcUeu7FFjdhiiWKLvYuK2DWIsTewJGqsgIiAIr1J72X7sn16n/n9cZ/n7syyGNNMfvl6Xi9fIs7O3jvznPs855xPGYOzBzOmX1pe/7qQORWJRDRrgB+LXTt3UltdTV63Dd1oNKLT61mycCF/uOsuQCW3vvrCC+R5PAwYOJDlS5ficDiIxmI4hfxQMBiksKiIy669linPPktjQwPRaBSjWG+KTkc8kaCsooKA309RcTFXXH01f/3wQxYtWKDCgJNJjfui0+lIJ5PorVYMqRQXjh9PR0cHb732GghUpKxmjEYj0r7X5XJx0KhR+P1+wuEwnZ2d2B0O7Vnj9/lw5+aqPKvOToYMH05dTQ0zP/+cYCDAju3bVQtrASNWFIUBgwapLXefj6OPPZZDDzuMpydN0kAG6XSapMgpaQ52+dVXU1JSwv133YWiKJSUlpISJOyN69ez35Ah2qYif0+/AQP4du5cpr/5pnYgA3X+2NDQwNDhw6moqKBXeTlHjBmzt2kg6iHt3131/1dtKJnERZ1Op5XZPxadAj3V09ZjMpk0EuOa1au5+fe/Z+vmzezeuRO90ahtJgajEa/XS1VVFcXFxTzy5JM89vDD7GlsxG6zERfSJ2kxAEwlk3S2t2M2mzn3wgv5fv58FsyfD6jDy1AwqHFZrBk932NPPJFIBnxWytvrhExLMpmkoKiIaDTK03/8I2vXrOHrWbNUxeJwWJPuRq8nEY1qpbfT5eKAgw5i6nPPsXH9eg16LLXJTCYT0WiU5uZm3G43vz7/fA48+GDenzZNNSsSlqGKGMTK7yCZSLDfkCFMevBBIpEIdbW1rFqxQquowqEQ9XV19O3fn1AoRHtrK2eddx5Wq5X3p03TqigZer2ePI+HHdu3c98jj/zo9/oLyutfE91z6qcc0jra21WtrR7+n9ls1uSLNq5fzx+uv54N69apQ3UhSR+JRDAaDHh9PqqqqsgvLOShSZN4UVTDNjFbBFWuHplTbW2YLRbOOvdc1v7wA3NmzSKZTGKz2/H7/Roh0Cbaznq9niPGjcNqtTLhd7/TWt5pUenrDQaSwv43HAzy6JNP0tjQwNTnn8dkNKrIK5HTSEFX0bWw2myMOuwwXnvpJTasXYtJ5FQkHFYRXSKnWkROnXbWWRx25JF8+P77qqil0diVU+k0iWhUnbnEYgwaPJhXX3yRgN9Pe3s7qwXKzSjev7amhoGDBxMJh2lpbub4k0/G6XLxwbRp2IUthgydXk9+fj7bt2xh4mOP/WgrMxqN/s2Ozz8b/1UbSibJSsoi/K0oKy9XTwI96A7FolEG7bcfG9ev55brr1cVgYXKaDKZJBGP07tPHzra2wkEAow67DCuuPpqlixcyPIlSwjKQaF40BpE6Wqz2bDabJxyxhlsXLeOP0+frmHb5SLSg7ZYTSYTAwYNorCwkE8++khrFyVTKVCULBRHMBDg1xdcQL8BA3jg7rvRKapLZG5uLp2dnSiKom4AOh12oZw8ZNgwXpkyhVXLlhEXlr/SW9okoZoDB3LFNddwxJFHMuXZZ/lqxgxARbtIJJ3cKORDp7S8nJUrV/LdvHmqD4oo7aORCPF4HINeT1NjIzlOJy63mwsuvphEMskLzzzDlo0b6dOv317fic1mo7GhIUuqo6f4pUL510QmGViSC/9W9CorI5VMquuzW0TCYQYOGsSO7du56dprqaurwySsZlPCgrZ3nz54OztJ+P0MP/BArrr+etavXcv38+cTCoVIZ+aUwdA147NaVXmjxkbeeu019RpETmkPynRaswju06cPfQcM4KMPPkBRVNvuRCJBWuSU+vI0Ab+fX51+OgeNGsVvzj4bJZ3GYDSSm5urwfn1ej0mAb03Go0MGDSId159lZVLl6ooU51OJUWazRhNJqKRCH369mX8lVdy7Akn8MrUqTzywAOAOhdMJpMa01/mVDqdpqC4mC1btzJrxgwa6+q0dlksGiUei2l8l9y8PHJycjj3wgtJAy8+/zw/rF5NeUXFXt+JxWqlec8eQsHgXm2uzJAb4b8z/ms2lO6MXSVjUXSPluZm5s2ZQ0tLCwMGDmTg4MFs2byZsvJyrZz3+/1YrFaOO/FEHps4EZ2iKpu2t7Wh0+kwiwet1+vlyLFjaaiv5/c33MDsmTP50/PPq5sDahsnnU5jEJtQKpkkBhwxZgwFhYW88dJLKopEzEAQg71Mhv1RRx/NeRdfzOSJE2lqaKBT8FVS4qGaSqWw2e3E43HyCwq48pprNKRKLBrVYMeSGxCNRrEL5IicRaSSSW1AGI/H8ft8OF0u9VSXVl3txowdy4fvv8/GdeuwORwkEwmqqqqoqakhmUzizs3FLgaUkUiEK66+mt6VlSo232BQNcXEPMggqqaBgwbx6vTp1OzezeSJEwmHwzidTnw+H2tWr2bAwIFZSsXRSETTXvqx+GVD+eejOxn4x3KqTbgoNjU10advX4YfdBArly/XRFMBbRZ58mmn8crUqZrkSTgUUnNKOJS2t7Ux5uijaWxo4Pc33siypUt57oknNHRVNBYjLTYTuXYj0Shjx42j/6BBPHj33STi8a7DjaKQSCaxCXRZMpnk4MMO48prr+WRBx5Qc6qjY6+cstrtJBMJ3G43106YwLtvv01ddbVa3bS3a5WMBCvYTSatApDISOncmIjH8fv9ak6JjojX6+WoY45h1pdfsnL5cpWzkkhQ1bs3u3ftIqWoMk45OTmakvh5F13EoEGDqKupQQFtM87Mqao+fXh9+nT2NDXxyP334/f7cbvd+Hw+1q1ZQ/8BA8gV3BRQc8VitWqt533F/5kZSk8s+H2pnc6eMYPHJk7sGtAJLkhZeTkte/aoPy82jwcmT8Zmt7Ng/nxcQnakRbR4QG2Jtbe1sWfPHvoPHEh9bS0vPv88wUCAlND9ku2qZCpFfn4+Ab+fPv37c/5FF/Hb884jkUyqCC3hQU86jU5RCIfDePLzGThoEL+7/npuvf56mhobsYvhoGTggipfEY1GsVqtXCzQJ2++8oqGxtGLh28ikcCRk0M4HKZXWRlXXnMNf542je1bt6ry+6EQOr1em4dI17lYLEa/gQMxWyx88emnVO/erSkYp0WVlE6l8Hm95OblkUwkGDV6NCedeiqrli3DarWqVVRGG1In/CmsNhs2q5XnnnhCm1slEgnKysupra5m+7ZtHDBihJakHZ2dXH7VVX+zj/8LD+Wfi57IwPvKqQXffsuDd92ldQQURUFvMFDZuzd7Ghs1CRCrzcZDkyeTX1DA3K+/1vx/UqkUepFTRqMRn89Hc3MzZeXlhEIhnn38cU0OxWK1dqExBUoyKGR/Lr/mGi694AINidY9pyLhMJ6CAqp69+am22/njptuonr3bnJyctQ5jPg5EDkViWC1Wjnv4ouZN2cOU599tuvQqtOhUxQS8TgO0aYrLCrimgkTeH/aNKp37qS+vl7dRMUGJVtWUim8vLyc3Lw8Pv7gA3bs2EFUsNrTQqMvlUrh7ezEk59PKplk6AEH8OvzzmPT+vWa/p9s7afp0v4ymkzkOJ3cftNN6ITUfjwep7yigt07drBj+3aGDR+u5VtbWxvnXXjhXkTu7hGNRv/lHivd4z++oeyLBd/TaaqupoZJEyficDiyHjbNe/YQDASY8uqr1FZX43Q6OeDAA1m0YAET77uPPcKzQ5bYEeFPoKDCc21WKweMGMHlF11EW2urxig3Ggya5WcymSQQCJDjcvHoU0/x/BNPqJ4Lej0pcWJRdDoQ5XQiHqe4uJhTzzyT+++4g+rdu1U73gzGrCKQKlbRQqusqmLlsmWsX7tW+2wATaZCURQCgQClpaVMffVVZn7xBZs3b8bpcuFwOFSp/VRKnSml04RDIZVV7PHQp29f/nDddfywciVmi0UjZ0p9JafLhUWw/A8ePZqN69dz1fjxtLe1EQqF1I1btO9S6bSaPIqC0+1m8cKFdLS3U1tTo86PxKYm2y0S8QJw2BFHMGbsWOrr6jQtpZ7ilwrlH499kYF7EnxsaW7mgbvuwmKxZA1yW1tbaW1pYeqrr1JTXY3D4WD4QQexasUKLr3gAupqa7U2jsFg0Dw/FMGv0Ov1HD5mDBedcw57mpowGo2qBL2A2sbF0FxKEE1++mlemjIFn8+HQbTmZGssnU5rxEGPx8PZ553Hg3ffzfatW4nFYkQjEU1sUiIqrTYbFquVsvJytmzcyMb160kJ9r2CmlN6mVN+P0UlJTz/0kusWLqULZs2kSO5IyKnUBSUjJzKzctj8JAh3HbDDSxftgyzyaRJtqTTaSKRiEooNho55rjjGDl6NDu2buXayy6jvbOTQCCQpeklfyadVoU0ly1ZQltrK7XV1fiFr70UuE1EIuzetUulOigKB40cyYmnnEJ9XR2FRUX7VBSOx+P/27DhH2Ps9jRAnPnFF1prJzMkkSoaiTDu+OMBWL92LU899hj5BQWUVVRQV1ODzW4nGAxqLmler5ejxo1jwq238pszz8Tr9RIXXBC9OM1Jja6Ojg4qKiq45c47+dPzz7Nk8WKSySQx0fdEwPhARW4pisKpZ5zBnFmzWPfDD6oyayqlIT9k6RkKhbDZ7Ry4//44xPW1trZiFogQeaqUrOFIOMyRRx9Nr/JyZn7xBclEgrbWVvU9RVktuQC9yspUSRivl+rdu2msryeV7vKSkWZHFquVzs5OTjnjDCbccgv333EHmzdtoqCwEJfLRfWuXSojVzwsFEXR0Ftjx41j3bp1bNq4UasWZTUSFdcyctQoho0YQXl5OXNmz+b6q65CJ2ZAF40fzzHHHbfX9//LUP4fix8jA/dUocz56isS8TjWbqggp9NJS0sLHe3tWk5t27qVyQ8+iCsvj4qqKnbt2IFdrNmi4mL0ej2+zk5GH3oodz/4IOeddhod7e0k4nF1cxAHjVg0SllFBR0dHfQqLeXG229n2ttvs/C779TDWyKh5ZTsREi5oBNPOYVvv/mG1StWaG0pWdGYTCYQD2ZFp2P/YcNU5nt7O60tLZjMZlICnJCVU5EIB48ezYBBg7jvjjuIx+O0tbWpXQqBdiStKlyU9OqFotPR2d5ObXU1HR0d6j3JnBIK5RaLBZ/Q3Lrpttt45P77Wb1iBYVFRbhyc6nesSPLagJU8q9ep2Pc8cezccMGNq1fTxr2zilF4YDhwxk5ejTllZV8N28eN4iq32azcd6FF3LSqafu9f3/Tzs2/i2SVU+Lv7amJktyXYb84KSe0Lfz5vGH665TT0aifNQYtkI4sZeQm544eTK33XgjgUAAh8OhsltFi0sODP1+Pw6Hg+tvuYWP//xn5s2ZQywW00pU7aQhTl4oCqMPO4wVy5fz7bx5apmdcY+KgB4XFRcTCoW47Z57CIdCvDRlCgaDgWAwqEF904LEJb0MTGYzF44fzzuvv87WTZvUDU1+VmJgn0wktJOV3W6nTAjs1dTUaNDkZCJBLBrFLEvgdJpB++3H2tWrtdPcquXLSaNu2N6ODk1JVq/X097WRnFJCaefeSbvvf026VQKk9BDSyST6HU6VWQzEODg0aM59sQTueW660in05rwZDgUYsrTTwNoDy0Zv7S8/v74R3KqrrZ2n/OsdCpFS0sLAEsXLeLm666jproao9FIjtOpobV0Oh3tbW2UlZfTd8AAHnvmGR657z68Xi+OnBziYoNIicpFHnLsNhtXT5jAN199xazPP9eQWxLcAiKnxAN32PDh7Ny+nblff72X1bXMqcKiIsLhMNfddBMmq5UpTz2lKkP4/epBLyOn5APWaDTy28su48/vvsv6NWuIxmKqRpjIbb2Y9YQEqtJht1NUVEQ8kaB61y6MJpNKYhaDeDnLSKVSDBw8mM0bN7JKbICrV65U5fjdbjra2kjE4+w/dCgms5mO9nbcubmcdc45fDVjBslkUp0LCeSlXkCPA34/w0aM4Mxzz+Xma68lGo1SVFSEIjbHl6dOJZlKceoZZ2R9n/+zxMafwtjtqUKRWOyeIpVKUVJWxheffsqkBx6gtbVVFaUTHgg2q5XKqipqa2pIp1KccsYZXDR+PF9+9hnLFi8mIrDocugnYb8mk4m8vDxOP/tsvps3jxmff64SsIQCsaIomp6O7In26deP4SNG8P60aWq5bjSSEEmSBs0fPh6LccSYMbS2tvLmyy8TCoUwm83Y7Xa1JEZNFKvFgtlsxpGTQ0lpKVOeeorFCxd2JWCGEkBCSN+PHDWKSU89xdWXXcb6desAVTRTKgJLaQ1QoZtOoXP03rRpbNq4MQuFJSuuVDJJY0MDDoeDw448kqOPO45pb77JogULNOa+/EwyQRZGk4kvP/10L6Vhq81GHjD9rbc46phjstpfsVjs397v/V+KdDrbvren6Knl1adv3x7nKvJhXdqrF3O//pr7br+dxoYGVUolkcDb2YnFYqGyd28tp8adcAKXX3UV8775hm/nzVNh7akUBr2ehKwMRPvK7XZz0qmnsm7NGj7+85+19SIh7KmMykSnKJRXVjLm6KN59+23tTlDQgz50+m0mlOJBNFolJGjRpFMJnlezG5ki1fLKXGSNxiNqnirx8PrL7/M0kWLCIfDPeaU1WZjyLBhPP/SS1x7+eVs3riRNBDw+YjJ9qJOp7WHURRcLhc2u50P3n2XzRs2qAetjJySCM/GxkZycnIYOWoUx518Mn957z2WL12qAXy65xRik5s9YwbBQIDiDF96i8VCQWEhH0yfzvEnnZS1gfxPSq/8lIUPXTOUPU1NrFqxAr1ez6hDDuGd11/fS76jo6ODyt69qerdmwm/+x0OpxNLe7um6KvT6QhHIlhtNoYOH45OUbj2hhv40x//yHuCdQpo7SLZr00BSjLJCSefjMPh4P133slqJ8gvV5NMUBRGH3oot9x5J3fdcguhUEhVLu4G19Tp9ejTaSw2G3aHgxVCziEWjWrDein2mEok1OF3bi4WiwW9wYBVsIb1BoN2ktLp9VoP22AwcM2ECdTV1lK9axdOoefldrtpaWnRNjmz2UxZRQWhYBCvz8ecWbPYvGEDPq8Xi9WqyXIbjUYigvx5x333cajQCXt28mR0Oh2tLS0kMiQx5PdqsViw2my0trWxeMGCHiGNVpuNPU1NNO/Zk8XK/qXl9dMjM6d+DOwgK5TWlhZWLl9OOp1m2IgRWISrp8Ph0F7b0d5OUUkJQ4YN48yTTsJqt2Oz2QgGg2qlLEQXjQYDB4wYQTKRYMLNN/PeO+/w1quvalVGTLS7pK9POpUiqSgcfeyxVFRVcc+tt2qwXEVwsQCMIm9Jpxl+0EHc9/DD3HnzzQSDQW1Gk9lmlkgpk9lMnsfD0sWLCQYCqn5fPK7mlCA5yira6XJhMhoxm0zYRAdD29Ayc0oQmq+ZMIHOjg52bNum+cHr3G6iwlnVIGau5ZWVhMNh2tva+P7bb9m+ZQterxez2ZyVU1Fh2vWHO+/kqHHjeOOll3j28cfR63SaQGUqmVRb8GIjslgsWKxWgsEgy5cuzWbVizCbzXg7O6mvrc2C7v/PobwkY/dvLXwZ77z+Ot8LwqB8/bjjjtMGVikhpFhWXs4Nt97KD6tW0dHRgdPlwu5wdJ0WUBEUjfX1uERJuW7NGj58/30UwCxISmnxxSUSCXJcLmKRCPsNGcJxJ53EhWefreHy0xnXI9EoOU4n+w8Zwo233sptEyZQvXs3RpMJBbKw9HLuYrVaOXjUKKqrqzX+iLbBKopmFxwWM5Yrr72WBfPnU7N7N+vWrMHv82naR4iBpM1mIxIOY3c4OPiQQ3j84YeJRqMqRFGcTuXpKxKJ4M7LIxKJ0NbWxoEHH4zH42HNqlXaZxAOhzVlWaPRSEdHB4cdcQTz5sxh+ZIllJSUEBZV1Z49e4jH4zidTvI8Hu3eE8kkvx0/nnWrV9PU2KgaHWVsVnKwa+p2cvplKP/TQqLqMi2x9xWKovDRBx8wb/Zs7WdRFMYecww/rFpFa0uL9iAtLCriultuYfPGjexpatJyyu/3a+vUYDBonKJjjj+e6upq3nnjDdKAQdglSBhvIh7H6XIRDYfp068fZ513HheefbY2R0iLYTliTSQFN6Vvv37cdf/93H7jjWzftg2TaHn3lFMWq5XRhx5KfW0tK5Yt09rB4uaJxWLqNUQimC0Wxl95JT+sWsX2rVvZuGHDXjmVTqWwORwqOspq5fAxY3j5hReIx2LU19Zq61fCn6UkUjQapbWlheEHHkh+QQEb1q7VkKKhUAi72ASMJhM+n4+DDz2U5YsX8938+WpOCRHMxoYGzTK5oKBAUyqIhMNccvnlPHL//ezYs0eFVNtsWTmVSqV6zKn/GZRXJmP3by18gE8++ojZM2ZQUlKSpWw6e9Ys7nnwQex2O52dnVT27k1ZRQVvv/46Mz79lKbGRpoFfFj2eKUWTigcZsDgwRQWFfHbc8/F7/NpGltms5kUXYPzSCiEy+3mieef5yEx45ALWIpNymommUzSq1cvTj3zTB558EHqamvV04X43VJiWxGvt1itlJSWsmLFCvxer1Zey89Jw/0HAhQVF3P19ddTs3s3386di8Vsxp6TQ8Dn0yokg05HTGzUjpwcDhw5kjtvvplPPvpIvX/QmM+JREJF2hgM7D9kiMpc37aN7Vu3ssbvx+f1dg1PxeeSFic6l8uFyWTis7/+lWgkwoL58zVop05UYe3t7eR6PISF2N/9jzxCNBrlhJNP5u3XXlN5C+EwgUBAk6IYMHDgXrpdPwfE8X8h9uW42FPMnjGDLz7+mOKSEq29mEwmmTt7NjfffjtFwrahrLycPv37884bb6g51dCgeQhl5lQqmSSiKOTl59N/wADOP/10Ojs6tJwymUxaCzmZShEWxLunpkzhmccfx+/zqTkl1ltWTiUSFBYV8esLLuCxiRPZvXOnKlMk8iMl4MQpcRgxmUwUl5Swbs0aOjs6sg6tWTkVDFJYWMjlV1+Nz+tl9syZmMWc1ef1duWUELSUpMvhI0Zw/5138smHHxIS0koyp+LxOHqDAZPBwOD99qOkVy+2bt5M9a5d2vXIFno8FiMhHvRJkVMWs7krp779Vr0GWemn03R2dJCfn08oFEJvMHDnffeRTqU4/sQTeWn7dpxOJ5FIhEAgoCHoepWV0ausLOv7/59ref3UzSSVSvHum29itVqz2NQGgwGLxcL0t97ivb/+Vfv76W+9xWcffUR5ZaVKKBIKn+FwmF69ehGNRgn4/Vxy+eU43W7uue02gsGg+tAXX1xEYNZzPR78Qk/o4cmTeXLSJJYuXqxVVvJ65MlaJvO5F17I3K+/Zs2qVRrLWN6lXqfTTjwG8SDff+hQ5syapW5UGfcuh4AG1KrK5nCwbs0a1q5ZQ0oMBsNCoTgi0Cd6gwG33U5ZeTmNDQ20NDdjF2ZCkmwplQRkOd+7Tx+e+9OfuOe229i5YwdGoxGr3U4oHFYHrRltPUn2UhSFqy+7jCXff08ikVC1wkQbQfazc5xORh1yCP0HDkSv1zPlmWfw+/0qITQep662Fk9+PjarldbWVgCOOOYYmpubyc/Pz7Ia+HsXfzKZZOTIkfTq1Ysvvvji7/rZ/x9DnuR/Sk4BvPXaa5qCggy9Xo/dbuedN9/ks9mztff59OOP+eiDD6iorGTXzp0aKCQcDlNSWqqRZ8/9zW/o078/d996K4FAIKvtGRXfoaegAL/fT4Gwpn3lxRf5bt687JzKqFYlouvsc85h/jffsGLZMlUuPpUCMaCX3Cz5u/oPHMghhx3GrC+/1OxxZSiKohEH9Xo9NpuNzRs3smXzZlKplGYeZrZYiAhVbL1Oh9PlorJPH+pra/F6vaqOn2hvAZrChcypiooKnn3xRZ59/HF2iZyyWK1YhIaaTsxZksJO2GQykYjHufm661i6aJEGkpE5lRT8FJfTyYEHH0z/AQOw2e288coreDs7NXBNbXU1noICbDYbba2tJJNJzrv4YpqbmykoKPhZc+pn21B+6qIHFUrb2dGBuQfmp81mY9fOnYC6mOZ/8w2P3H+/amMbCJDn8dAqOCcK0NnZicvt5qDBg7n6+usZd/jhxKJRdUgnynfJT4nH45iMRvLz87ny97/npSlTWLV8OfGMuYmchZjMZnVgqNNx3EknsXrlSubOnt11Wsy4ZslCT8TjXHHttRwxZgxXjR+v2fZmVjygDiIlmq24pIRkMsnObduy5O2jYvFJD267w0EqI7y8wQAAIABJREFUnaayqgqn08nOHTvQiYpKzndkqR0Khajs3Zscp5ON69dr7xcOh7GIE04aMAkJCl9nJ/V1dYw46CDKysq6TsUZPWej4N5YrVYmTp7Mn999l7dee43cvDwKi4pIJpO0iFOu2+0mEY9z5jnncPKpp5LjdNLQ0MCuXbvIz8+nuLhYQ7X8PfHcc88xePBgfD7f3/Vz/79GZsX8tyKVSlFXW9uF6ssIq81G8549WlWx+Pvvuff22/F7vQT8fty5uSqPS2xenR0dqvLuAQdw0223ccqxxxKJRLDb7WrVIXJKL8ivRoMBT14eV157Le+98w5LFy7syil5/aLSkPlz+JgxVFdX881XXxEXhxUZEoLvdruJx+Nc8NvfcsbZZ3PpBReoMvKifZ2ZU5rsiKJQVFKC3mBg66ZNJMSmBl0P3GQqRSgcJl+oV5RXVJCbl8funTu1eYp8ltmEkkA4HKagqIiCwkJ+WLVKfT+RUyYh1ZIGjAYD/QYOJBQMUltdzf5Dh1JWVsbCeFytTASRWnLU4okEil7PI088wYzPP+fF557DLXIqlUrRIlpeueL58qvTTuNXp59OnsdDQ0MDu3fvxuPxUFxc/A9tKH9vTv2sFUpPKJOewmq1qsY4PWh5SVnzdDrNU489xrtvvYW3sxO9Xs+epib0ej3llZX4Ojvxeb0EAwGuvPZaLr70Ut57+21VPwcVPaUTEEWJPjGJ0vfc3/yG7775hiWLFpFKJjWTKwlflKcHnU5HaVkZ8ViMBfPnq1WJSBBJLpQlv0GvZ9jw4ej1em645hp8Xi9G0RKQXglyoJ5Kp1V5EkVh944drF29Wh3Wkz27icdi2B0ObHY7l1xxBVs2bGDB/Pns3rWLsKw05ElOkN0k9HDI0KF8MG0a7W1tdHZ0aJ9vlmZSIkFQSM4PHT4cq83GhnXrsgaocvNLiwGpTqcjHArxwbRpFBQWaiQrvV5PcWkpTY2NnPCrX3Hyqadmfa/9+vWjT58+tLS08NBDD9HR0cEnn3zCaaedtk+iVmbU1dXx5Zdfcvfdd/O0gCL/El2h0+nIzc3F28ODIRaL4XA4MBqNvDRlCq+++CKtzc0YDAaaRU6VVVRoDqSBQIDLr7qKS664gk8/+ojdu3apPBNRCSST2Ygue04Op595JqtWrOC7uXM1xrnMKY3dLnKqqKgIm9XKF598otlASBBPpgySotOx39Ch5Hk8/P6KK+jo6FBnN3o9iVQqO6dSKVy5uZBOU1NdzeaNG1W+SsZBV854bXY7FouF315xBXXV1cyeNYsaIdkiEV3y9ZI+YDKZGDZ8OB998AGNQgom87PP3Ngi4TB7mpoYPGQILrebjRs2aPOslCADG41GTUkD1NbmO6+/jqegQNsUdDodRSUlNDU0cOTYsfz6/POzvte+ffvSu3dvWltbeeqpp6ivr+ezzz7j3HPP/UnD+X8kp35cUOlnDp/Px+6dOwmFQpzx618T8PuzNqB0Ok0gEOD8iy9m04YN/Hn6dFxut1YyWywW9Ho99bW1jBg5klGHHsqxJ57IdTfdxLNPPMHLU6dqp5tIOKy2xoxGlUErvsjzLroInV7P7BkzNGVRQBvwSXl3RVEYvP/+HDFmDOvWrFFRNkL/C1G6y1AU1YOksKiIDWvX4vP51J6ywLUr4sQj/+zOzcUpROqSKdUhUsk4FclTksTsV1VVsXTRIubMnk0gGCQajeJwOLQTviRaFRQW0qusDEVRWLpoEe+88QYtzc3atWY6Qer0eg474gj+cNddDBw8mOpdu/hh1Sp27tih9ZZTqRSRaJS4OF1pD6UXXtAYz93DarWy+Pvve/z+5cNk8uTJOBwOFi1axFdfffWT1s6NN97I448//jc1wv6vRSAQYPfOnfh8Ps698EJCweBeOeX1evn1+edTs3s3r7/8Mk6nU32gKQpmiwWD0Uh9XR3Dhg/n0COO4IgxY7j5jjt45cUXmfLMM9rcMRyJZEnGp8SB44yzz6awuJhPPvyQSCSitY1isRiKTqe1olAU+g8YwNHHHccPP/ygmsOlUhqSKyundKqacEVlJevWrKG9vb0rr8XBTsspAeHNycnBIqRiAoJ9ruWUmInIDbGyd29WLlvGjM8/J+DzaYgsnWhfg4q4ys/Pp6y8HL1ez5rVq3nrtddo3rNnr5yS1zFy1CjuvO8+Bu+/Pw11dfywahU7tm3T0HBShSIejxNPJHCIze2VqVNpb2vr8XBldzhYtGBBj9+/TqejsLCQ+++/H4fDwfr16/lrxrjgx+Ifyan/uPQKqEKOjz/8MF99+aV2wj/+pJOo6tuXFvnliIftEWPGcN6FF/L05Mnqg1MsklAwiCJO/LFYjLaWFhKJBL+9/HLmzZnD/DlzVLdDs1nzhJDyDrm5uQT8fg4/6iiGDR/OBWeemY3ogixMuNls5qBRozhq3DheeOYZ1eNaLLLumP6kUEY95rjj2LplC2uFe6E8tcies9Vm09oA444/Ho/Hw4zPP6e1sVElhmWQFxEtOoPBQDQWo6yykvbWVk2bKB6LEQwEMFssxKJR9XegJkBzUxOFRUX07d+f1StXauV7Kp3GKFSMpT3pUePG0ausjK2bN2eZaUVE9aMoCjlOJwUFBcSiURrq68nNzWX+3LnU1dYS8PsZOHiw5vgoP4+fUnabTCaefPLJn9Qm/eKLLygsLOSggw5ivkAF/l+PUCjEM5Mn89lf/6oNr8cdfzwDBw2ivq6ua6am03HQwQdz6e9+xxsvv0w0EsHhcGC12fD7/dpJNpFIqLa+6TSXXnUVSxYuZNbnn2sOouFQSLORkC3egFAbHjtuHOeceqrmpKrlFKqfh85gwGK1MmToUM749a955oknaG1uzrLlzVwFiUQCk8nE0cceS211NWtWrSIoLSPSaY24aLVaiYvfOeaYY6jq04dPP/qI1pYWTeFCatkhugnStKuishKf36+5OAYCAc0JMRaLqeriqZTWLszLy6P/gAFsWL8+K6cMQlBVpyjEEwkOPfxwqvr0YcumTRiFXAtARNgYK6gbRJFo+9bV1mLPyWHu119TV1ODz+dj0ODBWQiunhBdPYXRaGTSpEn/1pz6j28oqVSK6668knU//ECO8M9IJpPM+OwzKvv04Ynnn2fB/PmazMfQAw7glalTefu111RJhdZWdXhvMGhVRDKZpKW1lRNOPplEIsE1l16qQojFUM1qs2kD7Xg8Tkg4qT38+OPcdM01GpGPjNML6TQ6sThKevXirHPP5clJkwj4/YgXAV0nEcn1MJlM5Obl8e28eTQ1NGR7vIhZSzqdJhaN4snP55wLLsBssWjwRPGmahtBlOdyEzIYDFhsNnbt2MGmDRvUewTtOmT/Nimqh959+mgGQbNnzaKjra2rDSlaViZhV2oym+nbty91tbUqGCAYJNzaqm48gmglN8tEIkFjYyNOt5thBx6IxWKhrrqaQCDAzh07GDh4sHZN0UiEY7qx4ruHdrr7iTO3hQsX8tlnnzFjxgwikQg+n4+LLrqIadOm/aSf//85emojp9Npbp0wgcXff0+O06kxvWfPmEFRSQnPTJ3Kd/PmkU6lGHP00YwYOZJ33niDP/3xj7Q0N6s5ZbFgzMipRCJBe2srY489lpycHC79zW/wCQfTRDyORQipptNpEum0Jin0+PPPc+/tt2taX3LwLr9byfUoLinhN5dcwnNPPolXtIvSXTekDe2lA6I7L4/lS5ZQV1Ozz5yKCu2v0846i/yCAv70xz9qvkKAJickNw29yCmTyURdTQ0b168nKPxR5OcajUY1IUybzUZVnz6YzGasFgtzvvpKO1zKnJL8F6me3G/AADWnBOqtXeaUmEUmRbsrlUrRUFenaqgdeCCOnBzqhFbe9m3b2G///bXPJBQMctyJJ/5L19U/mlP/8Q1l5bJlbFy3DldubhZnxJWby7YtW3A4HNxx773a6x994AG+njWLgsJCOjs7u4Tp9HqKi4vx+/0Y9HruuOceAn4/N11zjcYIlxEOhchxOrUHXJ9+/Xj4iSd47KGHWLVyZRaLXyaALOFTisLFl13G1zNn0tjQoM150smkNpOBLvSJ2WKhqKSEuupqrXrRymy6UF06cdrftXOnusgy+CXyfeWg02a3U15ZSUNdHTFh8hONRrN8viWuPyEk8Y8cO5bW5mbVvtdgwC5aapKoKZ0l0yYTrtxcopEIL73wAg319QSE+rJsI8SEWZDZbMYpPsfK3r1RQO2Rp1JYLRZ1Y29u1tpsXq+XA0eOZOgBB/xLSVaTJk1i0qRJAMyfP58nn3zy/8Rmsq/YuH49yxYv7jGn6mpqSCWT3CocFwGee/JJPvnwQzwFBbS1tqLT64kKRYXi0lINXj/hlluw2e1cd8UVmgxJUrxHKBRS1wKqRUF5ZSUTH3uMqc89x9KFC7vAJxktIJlTCnDeBRcwT5zC5bwwnaG7JX9GpyiYLBZKSko0gUoQGytdOWUwGlEAR04ODXV1bNm4UUOPyc8jmUyqFYoQk6yorNS4UnuamjSxxkxAjpyf5nk8HHHUUfi8XrZv2YJOr8dht6teKaJ61wlFDGMqhcvtJhaLMf3NN2luacHv86mHRNF2k8RUo8mE0+VCp9NRXlGB3mhk4YIFpJJJtV2XTNLR1qa2lA0GvJ2d7D90KAeNGvUvhQX/ozn1Hx/Kr1yxQkUydDuNKqJ9s3rlSg4YMQKAb+fO5YPp08nJycEm+vVJwauQGlp2u52jTj2VY447joOHDNEefHJAnRTDQ4DcvDwMBgPP/elP3H7TTWxYt07zbpbkIPnAlzpAp511Fls2bmTmF190nYxku0tsJlLIbdQhh+ApKGDenDmEQyHt9drnIOC8ko2bX1CA0+lk4bffEo1Gs9pnknCVSqXUk2M0itvtJik4M7IlqEGV9aqJVzwex+v1YrFYiAnVZAC/z4fNZtPsT9NAUXEx+fn57Ny+HYvVSnFJCU2NjdoAVFZqik6HUQxgJz39NDM+/ZQvhU2qxWrFbDQSEddvs9kIhUKUlJZy+Jgx1FRXc8VFF6HX6znksMM4/+KLKezB4/yngDd+iZ7jh1Wr9p1TiQTLly7l0COOAGD50qW88fLL2B0OHA6HpkdnENVJKpnE7nAwZuRIzjz3XEYNGaJCxs1mIgJerpnQpdN4PB7SwPN/+hMP33cfK5cv1wbv6XQaMjXnxGZy7AknsKelhc8//bQLiNOthSzngSNGjqS0Vy++mzsXv9+fBWiRB6pUKoUOtfrxeDw4XS6WLl6s2WcDGt9FHtQMej3xWAyn06mSFEW1lTk/kEz8ZCKBTzDfpQkYgM/v1+wp0uJ35BcWUlxSws7t2zEYDBSVlNApIL/y8Ie4fqPJhF6n44FHHmHZkiV8+MEHBPx+LEJ6KRqJkEqnsVgsXTl15JHU19dz1SWXoNPpOHj0aM6/+OIs1Ql5v5mV4b8r/uMVilUYUfUU0oc5Ho9z+403MuOLL+hsb1cHyUBhYaHqwd7ZSSqVoqO9nYsvvZRb7ryT5x5/HJ/Xi8Fo1MpJecKRirvu3FwuvfJK3nntNTasW6dJRBsyEF2AhhQpLCrC19nJrIULNfy/VtrStVGYTCb6DxqETq/n27lz8Xm9WUNC+bCUPgvSU33n9u1s3bQJr9ervR90oUkcAhp80fjx6A0G3p82jc7OTk0qXp7SoIugmUwmKSktJRwOs33rVq1sB3UWYhC+FElhIrRnzx4MRiOjDjmEjvZ2dTaTkVRWm00lcwkIc35+Pn6/n4DPh9VmwyBOXGazGUWn+r68Nn06ddXVPDZxIna7ndJevUilUixZtIgN69cz6amnyPN4tN/xz2wmY8eOZezYsf/wz/8vhNls1nga3UNRVHXpZDLJ/XfeyccffEB7e7v2EPTk56M4HBo5sKOjg/Mvuojb772Xt159lbbWVjWnBKlPPqRSKVVF2J2byzkXXMBnH3/MimXLtMOVlDNKoyKBFNT1n+fxgKLwl+nTtVZzTzllNBrp078/TpeL7+bOpb2jIxtKLF4vlSCcLhepVIramhp2bN9OR3u79n4y4oKFnk6lOPfCC3G5XLz79tu0C/MteZCS7eB0SnWGTQjSZSKRYMumTeq9x+OqeVgk0pVTgifS3NSETqdj9KGH4u3spHnPniw0pcwpSQB15+URCoXweb0aF0/mlE4QLl949VWCgQAT770Xs8Wi5dSqFSvYsG4djz71FEXFxf+S9fT35NR/HBIzdtw4be6RGdIDZMzRR/Pqiy/yzezZ2MSHq9PrMRqNtLa0kF9QwMjRoynu1YurJ0zg1rvv5p5bb+XN119XJRmiUUJCBl5rYYmFcemVVxKPx3nnzTcJBgJaGRoX+lsy0qkUxaWljB03jk0bN3adijKTVpw2dDodOS6XhuKQEvSyEpGvBXXxW6xW1RsikSAQCOAT7OFM9Im8Z73BQHlFBXV1dXz8l7/Q1tamvQa6kkqn02G12XA4nej1egKBAMsWL1Y1t8S1A5qKqYLKDTnsyCMpKS3F6XKxaMECVq1Ygc/n63q9kLiIJxIY9Hpcbjfff/cd27dtU2HEGRVVWrzeZDIR8Pl487XXcDqduEUbRq/XU1xSgrezU7MjzvzufwpU+JfoOY4cOzbLDE2GhPOOO/54pr/1Fp9/8gk2u11tuer1GA0GOtracDmdHDx6NCWlpVx86aXc/8gjTHrwQV549tl955QY/J9/0UXk5uXx0pQphIJBfF6v9jOZPvWpVIr8wkKOPfFENq5fr1Ux3XMKReWq5DidHDBiBLXV1QSCQQ2W3z2ndLIyFy02v9+vMfL3yilxyCsuLaWluZmP//IXWoW6smydSfCCXqeKtOaInIpGoyxdtIimpiaN2S7vS/63OzeXQw8/nLKKClxuN0sWLWLVihWq5H3G62PRqFZRunNzWb5kCZs2bNDuUUYaFVptMpsJBgK888YbWKxW8vLytHsqLCoiGArxxSef7LUu/t3VCfwXbCiVvXtzyeWXE/D58Pv9xGMx/D4fQb+fU844g+KSEt554w3NhCqzp6nodNRWV2MQAm8nn3IK77zxBiuXL8ct1HPl6yRCy2qzqXLVl19OSWkpd996qya3IENRVMtRg8GA0WjkkMMP5+bbb2fWjBnU19Vpzmyah3zGIFCn03HSKaewY+tW6uvqCIfDGiEx02VSth+MwhPeJOx7pV+KDPlno9FIMBDAk59PrsejGlmJk2EypfrdZ2osFRUXqzDElGovnPlwkb/fYDBoCCwJAa7ZvZv62lpiggiWKX9iNBrJy8ujqKgIvcGA3+fjs48/pnrXLhKJBJFwmIhgBUu0UEFREa2trTQ3NfUoDunOzd0LRvyLdP0/F0XFxVx7ww0ExcM0HosREFXkuBNPpG+/frz16quYzWZVal3M2yQ3q7a2Vq1CTCZOPfNMPnz/fb7/9ltcbrcmhCpzymAwYLPbMRqNnH3OOQwZNoybf/974oLlLUNRFE3BwmAwcODIkdw3cSJzZs2itrpaY7enMnMENLfGY088kV07dlBXU0Okh5zSACuZOSVaWhLE0h1+bDSZCAWD5Hk85BcWahuPzBu9AOGAeviTJ36ZU5kHKJlTejHUVxQVKRmLxajetUvNKSGXb80gbBuNRnJFThlNJvWA9eWXbNuyhUQyqfHJIiKnbDYbRUVF+H0+du3YoZpsdQuPx8PihQuz/u7naiH/x1teANfffDNDhw3jrddfp66mhgGDBnHJ5ZcTT6WY+uyz7Nq5E50YtLmEt7JkwoZCIVqbmzlszBg6Ojp44uGHNckHo9Go9UgVICKY3U6Xi2smTOCic84BugZ0mSEH6vkFBVx25ZVMvP9+fJ2dXcO2TFE78XcGoxGXy8XCb79lp9Ae6jHSaewOB7379sXldPKDgD1mzpjk5iR7vjqdDr1QYP36yy/p6OjYi+8iZyZy2OnJz0enKIQE5DGzhypbe7K0zy8qokiU8YpORzQSISE2FVlBptNp8jweGhsaCIdCHHDggZRXVJCIx9m2ZQvJVIqi4mIV9WK1qkQ3UVXtK7r3qeEXpeG/J/Z16rzsd79j4KBBvPnqq+zasYO+/fpx8WWXoRgMvPHKK2xcv14bXjtdLrU6F12BWDTKnsZGDho9GtJpHrrnHkLBoMpJEbMPeXKPRqMYxQP2D3ffzZW//S3ptCpdouVUxpzAZDbj8Xj43e9/z5OTJtEmkIa6bhWVJAUbDAacTierVqxgx9atGgy4e6TTqqlcVe/euHNzNQHVrLmtqIAkt0Wv06EYjVgsFmbPnEm7qPizcspiUZXHxUzVIzTnwsGgBkzQKAPpNJYMjbTcvDxKevVi2ZIlak6JSsSckVPSBrm5qYlQMMj+w4ZRXlkJoNlIFBQWYrFasQpOUCwWo9+AAftcEz3l1M9V9f9XbCiKonD0ccdx9HHHaX8XiUQ49/TTNTy6FFbzxuPkeTzEYzFCwSBOl4sHJ03iL++/z/mnn649mKWYo9Fo1Mx30qkUvXr14tGnnuLJRx9ly6ZN2RIQmS0bUbpeNH48M7/8kqbGRq1d1P1hL//bZDJRXFpKa3PzXtIPmfeq1+vVuUYohK+zMwtpJUXvZJgEVj0ajZJIJPhh1aosaYnuLF93bi4DBg7Ek5/Pd/Pnq0Qv8Z7dN5SU4KfI6mf2zJmEhDw4qItQ/lkORTvF9e4/dCjRcJhlS5aojnJGIzGhM1ZSWqqeiEMh7rjvPoqKiymvrKS1uTnLZhags6ODE3/1q6y/+zmc5f4vxOFjxnD4mDHaf8fjcS48+2zq6+pUm2oxN/N2dpKbl6cqTIdC2Gw27n/4YWbNmMFZJ5+s2dVK/pZko8cFH6OopISHHnuMl6ZMYcPatVmqDpnzkHRa1ek6+/zzmTtnjiqimtGC7W4AptPrMZpMlPTqpbmSyhloZsjckRpjdbW1Ki2gh5yS8xy73a4empJJVq9YoRILM+Y28rWxWIxct5vefftSUlrK9999RzAQ0KyEswbd6bSm4Scf/LNnzMAvhFzT6TSIzzzzoe/t7CQciTBov/1IJhJaThmNRpVTFgzSq6yMYDBIZ0cHN/zhD3jy8xm8337s3LFD2+RktLW17WWu9XPl1M+O8vqp8c1XX1FXU0NFZSWBQICWPXvUAR/g7eigoqqKgNXKHffei9Pp5C/vvYdBMF1lyH5mSWkpsViMwqIi/nDPPTx8331s37Ytq4KQEiwSeaUoCqefdRZ7mpo0A6DMyJSCSAO9+/alorKSjevXawJt8p4zuSmS3yLdGrcJgTr5T+bnJKuHZCKByWgkqdOpRkLdJGnkJqXodAQDAUwmE3aHQ5OJ0CtKVpWh0+mwWq0Ul5aq0ONYjEQshkkAIDKvO/N68gsLqaqsZIPfz+ZNmzCI2U5atN3MZjM+r5eW5mYqKisZftBBfPrRR7wydSo5Lhd7mpqIxWJ48vNJJBK0trZSXFLCsSeckHU/v7S8/j3x/fz57Ni2jbKKCsLhMI319RqnwtvZSWVVFYFAgOtuuonyqireef11tTI2GLJmBMlEgoKCAhKJBE6Xi/sefZSnJ03KnoVkoL9kTgGMO+EE0qkUf3733b3WcRZUHygvL6eqb1+2b9lCa2vrXijJzH/rdDoKCguJiupKVlGJjJzSWl6plCanJO8vLvgmmXMWqUYRCAQwCYO7SDisGlxlVFXyGqw2GyWlpTQ2NGhsd6PQCMy8bhAMekUhLy+Pqj59WL92Ldu2bkUvnmHywGcymwn4/Rr8/tgTTuCb2bOZ9sYb5Lhc2lzUk5+ved14PB5+ddppWZ/tz7Wh/MdnKPuKmV9+qWG6CwoKyM3L0xazdGE87cwzOe/CC3n8kUe0DUHKPkDXicckFE/veuAB3n3rLXbt3InNZtNO+JmL02Q0YjAYyMvPp7WlhelvvbXXZiJDIrp69+lDrtvNwu++Uz0MMhJFq1DE7zKbzTgcDrZu3szWzZs16GP395Wii06Xi/2GDkWnV13vMofk8rWSQS971GUVFWzetElb8LKXa7FYuvSQjEZVWkY8FCw2G8FAIOt99Xo9ZvEzsvSu6tdPNTkSp1NpSyzVj0894wz+OnMmBxx4ILNnzqSjrQ2n00lQ3KfZYqG1tZVwKMSpZ5zBg5MmaWZFMn7xQvn3xOyZM9WHZzxObl4e+QUFJMWhK55I0NHRwbjjj2f8lVfy5KOPajwJU7ecSgrJIo/Hw2333MNfP/iALZs2YXc4eswpo8ip3Lw84vE4r7z44j5zKiXWVEVFBaVlZSz+/ntqa2qICWQi7J1TJrMZm93Ojm3b2LZ5M16vd692czrdRR60OxzsP2SIavWdTOLzevcafkvKgNwU+/Trx7YtWzS1YC2nhC89qFV8VEjL2B0O7MKQTDsg0tVKl7JJnoIC+vbvTygY1FQtJCHabLGoROBjj+WTWbM44qijmD1zJi3NzThdLsLBoKYa0N7eTiAQ4MRf/YqJjz9Obl5e1v3/XIe0/4qWV08hF0QsFiPH6STH6SQYDOLt7CQUCjHpyScZe+yx3HHTTcybM4ekGGBl8kZAXRh+n4+rrruOYDDIvK+/VlEq4kQiNx2ZAMlkEqvNxsGjR7N29equU1G3lpgc4tvsdnJycqiprVWd25Qubklm+S4lGGTLwOv19ih+KcPldmt92NrqarxifpNZ7GeWzQaDQSul53z1Fd7OTg0eLF8rxSF1ej3u3Fxyc3NJplIEfD6aGhu1gWjmz0ivbL1ej0Gvp2b3bg2aGY/FSJrNGqwxnU5jtlhobGzkm9mzKS0t1a7P7XZjt9tpb2/nlbffzoIJdw8plf5L/GtDVs2RSASn04nT6aREtFFCoRD3PPggp511Fg/efTdffPqpynGSbS6hoADquvB5vVx65ZU47Ha++PRTopFIjzkFavVvtlg49Mgju3KqW9tYhqIo2Ox2nG43u3fu1FrSqZ5ySiC1ZLu5p41EvCm8ZdwuAAAgAElEQVSk0xqZ2Z2bS0N9/Y/nlMjTRDwOisLXM2fi8/nUCiXjtVFhwy3FN/M8HpKpFH6vl0aRU5kzHARwIJlIaMi6apFToBKRM+230+k0RrOZ9vZ2vvz00yx/KKewGG5paWHqa69RkmEF3D1+rrnkf22Fcsjhh2tIIXnqcTgc5BcUUFJayvEnn8x9t9/OgvnzsVite7WJrFar1tt/aNIkPPn5XHPZZdpJR85CJJRSL3Sshh5wAE/+8Y8sX7qUhvr6rtdLzR8ZYgOqqqpSDaN8PnXxiAW/l6aXWOglvXphNpk0BdTuodOpYnk+n09DhMgeNoqioVlkSEKUPDkmkqpnSncejSzhQZ2ZVFRWotPraW1uJhyJZG+W4jqMwnVPcgl27tjBurVrtXmUdH6MCoFIu83Gjm3bmPrcc+rm3m0wKCUl1v7ww49+979UKD89/p428ujDDycWi2EXkusAdrudwqIiCgsL+dXpp/PoAw8wR0D0M2V8EomE5rRpMBq59a67GDR4MJecf76GetTgw+J0Lw9RAwcP5vkXX2TlsmXU1dR0oSS755S4n169emnItEzLhu6vlxtkYVGRprGX6HYoku9pEKhEk8mE2WzuyinoOaeMRg1FmUqpTouZVVJPOVVWUYHRbKa1uVll2aeynWllbsucIq0qH69ZvboLTJNS/VliIqdsdju1u3cz5emn8QcC6LoJrsrnyJrVq3/0u/+5cuq/pkIJh8PM/fprli1ejNVqxSO8BWRPUKfXEwqFCAWD3Hz77TTU1bF08WK1d5hWVYjJOAnI3mZhYSFHjB3LCWPGZJ2Kus8IbIK3cd/DD3PrhAl0CMRHVmQM4PQGAzarlT3NzTQ1NPS4OciQMF6b3U57Wxvt7e3aQG/vXyFaC2LOUltTs5fqMqARrpLJJBYhc2K320mJwad0gZRJrhfcHakYMHDwYFYsXapenyBYWqxWTQEZUFsiyaRmCyDtgIOBgGYW5s7NJScnh2AgQFtrK22trexpbKSuro5EPE7/gQM1ZQJ5f/tEv4mIRCK/bCj/gohGo3w7dy6LFizAZDZTUFxMqRhwe/LzMRgMhEIhAn4/V19/PQG/n3lff40nLw8F8Hq9Wc6HEmHldrs549e/5sSjjsqqHDLXm06nwyZ86B+aPJl7br1V88PZC3klc0rwsrw+Hw319XtVzPL1oOZUfkEBOU4nXq+X1ubmrP/f088oikJhcTGNdXX4xL1lhryOpKiopPhjWrTa091ySh66JKt+8P77s+6HH7R8S6VSmIVwprxfT34+KApNjY3oRDWmUxQ1xwVU2e1243S5CAWDtLa24vP5WLN6NXU1NcTjcQYOGrQXcfXHuh1yLfyfGco3NTbyu0su0WxGJUTxwIMPpri4mG/nzlUZ3yUl/OHOO+k/YACPPvAAtdXV7GlsxJWbi8PhyJI3iScSFBUV8dYHHzD1uedoamxU2a7sPdST8L2Lxo/nvWnT2CMshDOrnswwGAyYhBWvZBTvK4xGI46cHFKpFPUCfZJOpaA7k1lR0IvSPZVWSWJbNm3K8ovIjLTYABTUB39bW5u2cCWyJvNeAc28yJGTw8ply9i2dSsoiobKSQaDWR4VEu+v0+koKCoiLtpfBiF1E4/HVXBBMEhnZycWm42hBxygDuZbWmhrbSUnJ0eTgZAPncFDhvzYMiEej/+yofyT0d7WxlXjx1NbXQ2KCq83m0wMGzGCQw4/nHlff00ikSC/oIDf33gjw0eMUHOqpobGxkbcbjeOnBxCok8PXbpwr02fzhuvvEJ9fX1Wuzjz36lUikQ8zlnnnsvnH39MQ319j2hKGVKU0el00tHe3uNrMl8rCYYNgusltea6hzxISSmUrZs2ZVU9mZFOp7FYrepBtKiI1pYWwqFQFpoTsp8fmTm1asUKtm/dipKRUwmRU7IdremDAQWFhSQTCWIZOZWIxyksKiIWi9HR0YHZbGbY8OFEIxHVZ6W9nfq6OioEFF9ubPsPHfqj6+Hnyqn/igpl4r330tzUhDsvj2AwSIHDgUGvZ8nChTzx3HPc9cADRKNRcnJy2LJpExOuuorOzk6t19nW2oqiKFRUVdHR3k44FOLg0aN5+IknuPGaa1i3Zs1ePJPukN7f/Pa36BWFv/7lL1mlbfeQc4L+Aweyp6lJK9/3FYlEQoPKejs7u0r27hVHxvvrFAVFsIrlwsy8bhAw3nQak8WC1W4n1tiYhRLLvDeJ50dRSEQiKjIkHs86OcnfHYvFMBiNWMxmcj0e2tvaMJvNNDc1aZus3BgUnY7Wlha8nZ04nU48BQWsWrECRVH9V4LBIHW1tZT06kVU/N7jTz4Zl8tFIpHIsqLNjF9gw/98TH74YaqFW18wGFTJhwYDK5Yt46RTT+WOe+8lEomQk5PDrp07mXD11bQLToiiKJoKQ0VVlTZjGTJsGE+/8AK333ADK5Yt6xFtmLmeTjvrLIqKi5n04IMqjHcfIcEt/QYMwNvRQUToy+0rkskkLrdbk4tR9pVTmcNwRXjXs3dOoagkStm+MptMqoJFhptjd46YXihX6BSFSCRCh/BjCYdC2c8a0THQ6fVYrVbyPB68nZ1YrVZampu1VnYmDaCttZWA34/d4aCwqIi1ouqRLrMNdXWUlZcTj8VobWnhsDFjKCgs/K/Iqf/4DGVPUxMrly/H5XYTDocxCFSJhOx9+P77mqqtoii88OyzKDodVX36qOJuqZQmoQ7Qr39/Knv35tGnnuKhe+9l88aNmHvQC5ODPJ1eT59+/Wior+fpJ5740c1E8kdGjhrFxvXraair22uj6h5Gkwm/GHqHQqEsNeLMkCceR04OdocDg8GgnWa6v04itSRap6eFn4m2kZug1PsyGY2EuykwAxqLOSEGhqMPPRSblPoX9y9BDwaDgfLycm649VbKq6pIp9PU7N5Na0sLLc3NBAIBdLr/x955x9lVlev/u0/vZ3rPzKSSIZBCQkhooTfx6hX1oogUkSIKytWL5dq5VkBRVEABFQEv7RKacKV3SCEJ6Zkkk0wyvZ5e9/79sddas8+ZGoTIz+v7+eSTZGafffbZZz17rfW+z/s8NpLJJF2dnWQyGRYsWsTGDRu4/KKLuPyii7j/3nsLiAMy/llD+dsiMjzMi889R2lpKUmhxC2b6VwuFw/++c9qN6BpGr/91a/IZrM0z5ihdL5cLpeirM+aPZvGpib+66c/5fof/IB1a9eOiSlN03AJT6L6hgay2Sw//N73xp9MRA2ioqqKo48/nm1bttDW1jYppux2O8lEgs6ODhLxuDp+XEwFAoTCYRxOp2nFWzxZWcgt2UyG0vJysyFaFNSLMQUokUb53i6XS5l7FVyrqJnI+uqy5csJhkJKGcBut6sGYIfDQcO0aXz+mmtomj4duyDB9Pb00CcUitE00um0+dkTCQ5fuJA9u3dzxcUXc+kFF3DvH/+ozm2Ng8Xy+rtPKIODg6YpVjYL4kuS4XS56O7qAkznuRt++ENWPvgg69euZf3atTQ2NwOoSaC7s1OxTzKZDKtef135LUhHOBmyJ8PjdnPUsmWsXbVqwjyk7LCvqa1l29atin1SXFSEkW22ZEYNDgwUsEOKw+vz4XS5lE99v8ibjgJW0XvZ7XZ6u7tp27VLballCsLKAJNNaDbNpC1nslnV5VtwesmiMQxKSkpMZztRvJSKyP5AQCmqJpNJPnn++bicTlKplPndaWZPjNfnM/O9LS38/t57WXHyyexsbcXldlPX0EAgGOR/7r+fX95446jr+Gcfyt8WQ4ODaJpp6KTn83iLMNUr9KqSySS/+cUvuPuPf+Tt9etZu3o19dOmmd3yYhXf093N0NAQ/3beefgDAV564QUluW7t+QIhSCpowieecgqvv/LKhJiy22yEw2Hq6+vZvHGjcnAcK6R+n13IIfX19k6IKY/Hg0tiKpejp6dH6YpZY9RC026nv6+PnTt2KGJLAabEZ7bbTG8lTfR0ZSWmiq9b09TEFAwGMYCY6OCXGRaFKbvd9B258EICgYCZ+ne7FaY8Ph/5XI7GpibuvPdezv7Qh9i5Ywd2h4O6hgbCJSU8tnIlN/zwh6M+5/8Z2nCd6G41DEOp7srIpNPMmTuXZDLJJeefz5aNG8mLHcng4CBDg4PMmTuXTCZDV2cn4XCYW//wB5LJJFdddpk5o2M+KF0uF2SzBZ25VdXVfOXrX+eXP/sZ3V1dE9ZC8vm8mdssfhiPVd8Qf1wul/nebveYOwKVb87ncQntpIwo8Fl9I2SoNJdwjZMyKWMWLxmRV/EFAgSDQTr371eqqNbzqlqSuDc2m43+vj6eEl2+ssAofcBl8VDXdZ56/HEGRMoRUJLeWSF9Ho/FSCQSvPDMM+phJY9raGzkrTVr2L5tG4fMnTvyvf9TemXKMVZdsqqmBsMwTa6KtZ7SyaTyo/ncZz7DW2vWqB3J8NAQw0NDzJozBz2fp6uzE4/Hw69vvx1N0/j8JZcwLIQN5W5E1hHkeCorK+Or3/wmt9x8s7I+GC90Xae8shINRlkTjwrxO6cQspwUU0LfTtYvMIyC3YYMOR5dLhdZQdm17vhHXYbok/H4fJSEw3Ts30+2qMZivY5cPq92QMORCE8+/jhDYhFtiHSYJNFIFthTjz9OZ2enmnAUprJZcvk8cbEre+qJJ6hvaFDNqS6Xi/pp09i6eTOb3n6b+QsXquvJChml9zr+7jsU3TA4ctkyRUWVIWsTHz33XJ545BF2bN1KeUUFHrdbfamazcaunTtpmj6dyqoqLr7sMnx+P//5la8AqOZAueLyuN2mr7SQe7734Yf5zS9/ye6dOyecTDRNo6KykmQyyea331agGg8ADrudcDismBpjDXwYGYD5fF4JJyp7Yl0fdX7JXZd1FpfLhVNQeouvV1J987kcNTU1eEWxUTMPKND6kZOItCOVonsekdaQE5fD4cDr9VJaXo6mmVIcv7vlFgYGBsjn86Y4ZDKpzJQaGhvJ6zqPrlxpmpyNcZ02TWPjhg0FP/9nyutvi2w2y9ErVpgLBMu4lt/Lv513Hs89/TRvr1tHWVkZPp9PNRTabTZ279zJtKYmKqur+cSnP01lZSVfveYassJ1UO5O5EMqGArh8XiorKri/kcf5Q+3307r9u0TTiaappm7YMNg/bp1ZiF+AkzZ7HaCoRClpaUHhCnNZisw2BuFKdHzYZgXZbYaTIApeX9ra2rwBwIjx42DKZ8FU7rAlN1uNyVaxDk9Ph9l5eU4HA7i8Ti//93v6O3uHo0pXVeLssdWriSeSIxJu3Y4HLy1enXBz/8hU17Fq6lEIsGmTZu47ic/4ZTTTmN4eJjBwUGGh4bI5fNc+aUvUV5Rwa9vuonh4WEG+vspFY1DciDkslna9+zB4/Xy4Y9+lLvuvJPI8LBp9CS2i3KFkEqnlS/JJVdcwU3XX8++vXsLVEWLr1cW0wKBAN2dnROuomStZ9q0aSQTCdO3ZbzjtRHFZE3T6O3pYVh07I41mFUx3mYzH/SiCz5m6W63hjyP0+UimUyafuDy52Kysnq9W90mZQNmOp3G5/er7uBAMEjDtGnEYzFy2Sxz5s6lsbnZzE+Le9jU3Mz0mTM5fMECgqEQC484gkMOOcRMJQjKdEa6S4oopkD+M+X1ziOVSrFhwwb+8zvf4QMf+hDDw8MMDQ0xJBpdL7nyShoaG/nljTcyODhIf1+fSm/Kzng9n2dvWxtOp5OPf/KT3HfvvQwNDKjULKB20dKdEE3jvAsu4PZbb2Xnjh1qPBSHXOB5vF7CpaXst7gujhWy1tM8fTrJZJLu7u4pYcpms9HX08Pw0NC4E5XElCYwJZUl4mNgytpjI6nCkpWqvFIEuUCG1CCU/7bZbKRSKbMPCFQTdWNjI4l4nHQ6zazZs5nW1KQmGJvNRmNTE9NnzuSwBQsoLSvj0MMOY968edhtNgYGBhgYGBhxbMVcpDuKGoP/4VNe2WyWdevWcdhhhxEOh/nhjTeyt62NTRs34vV6Wbp8OX95/HEuu/BCdrW2KglnTdMoFQY06XQaPZ9nbksLx59yCl++6ir++pe/qMHjtjidSSZFOpXixFNOoaysjOf/+lcTEOLLLt6lGIaBU7i+OUSRcqKQAySn66NyqdaQKzzF3WdkUI4Vks6Yy2ZxiuLfeOQBOflIsOSyWTr37y9IS8hzSs96TeR5s5hFREPXSaVSpFIpXG636bmNOSj3tbeTSCSorqlRHce1tbXs2rnT3N6L7yeVSpFOpTjv05+mcfp0fF4vpaWlZl0mHicvXCR1XWfhEUcUfIZ0Ok1FkeDdP2PyyOVyvPXWW7S0tFBaWsp3/uu/+Ozll/P2hg24XS6OXL6cF59/nisvvphdO3eSTCQUNsrKykgK+wE9n2f6zJmc8cEP8r3//E+eeOSREUy53Xg8HnNxJh6wqWSS5cceyyEtLXzr2mtJCf/4scROZRq2tLRU2URPFLKXQ5pvjRdjYUq+33jh8XrN9KqQIZoIU9a/8/k8Hfv3k7M0Msv3sgpjSuMtuQNKpdMjmBKNyJlMxsRUPE55ZSXllZXs37ePqpoaojt2mC6thkFFWRnpdJpELManrr2Wlnnz8Pl8hEWZIB6PMzw8jM/jIZfNsmTp0oLPcLB2/dqEeUsmfC4ecOhClE3XddasWUNjYyPVY9i/grl7+cDJJyuDpj1ixSQ7V2fMnq0K0U++8AJnrFhB265d5MXWUuYwHQ6HKSQZiZDL5/ny17/Oi889x8vPPz/ilS4GSrFkhM/vZ/6CBWxYv17JSkwUTqeTiqoqZdY10b1V3bhiFSc9pcc7Vq7qfD4f/X19Y0pRyGNdbjeappFKJpX8vGy+sh4nJ1lplepyOuno6CjINcs8rsvlwufz4fF6iQ4PK5ViEDRKh4NUIkFldTWVlZXYHQ6WLltGV1eXuo79+/ZRVV1NIBgkHo3S2dnJ/EWLOO/CC2loaMDv9wNwww03MGfOHD75yU9Oes9TqRTHH3+8UmP+6Ec/yne/+91Rt3DSEx28eE8wZRgGa9eupba2lrpxJDgymQxnn3oqRt60uN3V2mpiyjB7sWbMnIlhmBI9//vii3zkAx9g66ZN5u8tbop2u53G5mZTny2f5+qvfIXVb7zB8888QywaVWxCGI0pj9fLEUceyfq1a8fcCRSH3eEwfe2HhhRexwuJKWmFPSVMeTyq4ViqZoyJKYGjZDKpCDejzi+ZlbqO3W6npKwMr8fD/n37xsSU2+3G7XIRCARMl1bB9pKDVe5oysrLqa6pwW6zsXjpUgYGBkjE43g8Hvbu2UNVdTXBUIhELEZnZydz5s7lwksvpaGhgaBIpd966634/X6uuOKKSe85TAlXY2LqoO9QDMNg48aNVFZWjjuZALzxyiuk02nCQgOqo6NDeV0bhkF/Xx9ul4t//9rXWP366+zetcu0C85kSIsmJw0Unbi+sZHD5s9nz+7dvPbyy3j9ftKZTKHoHBAIBpV3wIxZs2hraxs3XyvDuivotvSDjHcsoCaTifj51nsGZk1I8t1htLyLPFaq/8oHvj5BGi2PyUQJBIPKX17mZV3CUyaVTJLNZFjxwQ+Sz+d58rHHVBrR7/djGAapdBqny8VFn/0sCxcv5r6772b1m29SUlqKw26np7tbGQz19/ZSWVXFv51/PsetWMHg4CCtra3ouk5NTY25iptiAdHtdvPss88SCATIZrMce+yxnHnmmSxbtmxKr/9HCMMw2LJlC+FweNzJBGDtqlXmSlj4rO/ft49UKoVTyID09/Xh8Xi47AtfYPvWrWzdvFmxpKQdrkzV5HI5pjU309TczPDQkCmBJMQM9aJVuz8QMA2r7HaaZ8xgz65dBXWNsUIueOw2G5379k2IKdmPItXGx6LNjnXPZNp3UNQAYQJMGYbpVOp0FhT+C67DgikDCAaDKgsgw+Vy4XK5SIod/EmnnYbX6+XRhx4yMxEej6rNSO298y+8kOXHHsv/3H8/69aupaSkBIfTSVdnJ9lMBrfHQ39vL2UVFXzu6qs54eSTiUaj7BbGd7W1tSQSCcon0M8rjneKq4M+oezcuROHw0GzoPyOF4ODgxiCIeF0uViwcCG7du5UBWCnw8G3f/ADDmlp4bOf+pTqMnUKD3mrkF3H/v00NjXxqQsu4POXXkoymTSd3QQDRBruaCINJOXdM+k06VRKPXzHGmzFKaaJtuVg7npsoiYx0bEyl23dZk+261HvL1ZfkvY41musgDCA3u5uJcsiAWRl8DgcDlLJpNmciblyzGQyppGWzYbDbicRj7Nk6VK6urpo27WLhmnT1PvVeL0M9PfjDwT4xa23FlxLRUUFFRUVJJNJXnrpJe655x727NnD8uXLqRdd9hN93kAgAJhp1Gw2O+l38I8UmqaxZ88e8vk8M2fOnPBYq/Wsw+Fg/sKF7N6507RbEDv1r37rWyxZupTLL7rI1JMSja6KLYU5drq7umhqbuaKq67iqssvJxGPj3h4WDGF+b1ITBmG2S0uHSKnhCmbDSaYULxCOXy8bnkZxZjSdX2U0vBYYa1fZjIZc8KYBFMa0Nfbqywk5O8VpsQEm0mniQk2qmyf8Ij6sFP0zRy+aBFRYRlR39CgzicttG2axh333FMw7svKyigTqedVq1bxu9/9jkWLFnHSSSfRJAy8JvvM7wRXB3VCGR4eJhKJsGjRokmPndbUpOhwYNZDWubNI5/P09Pdzbeuu47aujquuOgiukRxDEYGr8frVQZCzdOn8/0f/5irLr+c7Vu3moNarGJsNpsaaHJrvvy449i9Ywfbtm5VulPWRsGxIhgKTWlwZrNZwuEwdrudHtEpO+bgFAC36iSNN6nJ65P3yeVymXpbRqFUhDUkbTGfNy1SU6mU6s3RbKbviswPO10u8rkca9580+yu13V0UPdR9iV4vF5uuflm9ra1qVWWdRCWlpWxc8cO+vr6xqyReL1eTjvtND7wgQ8QCoX4wx/+wNe//vUJ7yeYk+3ixYtpbW3lyiuv5Kijjpr0Nf8oEY1G6e3tZfHixZMCvrGpCQ2UQZXL5eKQlhal1/blr32NQw8/nEsvvJB97e3qdbls1ixce71Kur22ro4f//znfPnzn2fzxo2q7gYUjFtd182GvmOPpX3vXrZu2lTgazIppoQ760SRzWQIhcO4RN+atRGxoG5YjClBPpkMU04h0joZpmQqKy+kXVLJ5AimZI1HMC2dTid5Ye6VzWQKmjNTQsEYwOXx8Mfbb6enuxtHUd8PmIrDnfv3s190zxeHx+PhuOOO4xOf+ASZTIbbb7+d733vexPeTxnvBFcHleVVUlLCwoULxx34uq6zbetWNm7YQMu8eVTX1jIg6IQyIsPDVFZVceyKFfz8Jz8BzF4WOTjVQM7nTRtgl4trvvpVfvCd77Bpw4ZROwldHxE5rKqp4evf/S7pVIp9QpzOurpQ/SHaiPlOuKQETdMYHhoqyBWPF/l8noH+fnq6uwulrYtC5lqVcqvTWeBhbQ3r4Ja9AcVmXdZ7Lvnu6jUi/SYZXRrmhNPY1ES4pMTc6Wia8k2RRU+X201FZSVV1dWmF0Q6TSadZnBwkF2trbRu3z6qbqNp2qg0QHHous5ZZ501pckEca3r1q1j3759vPnmm2zcuHFKr/tHiGAwyOLFi8dkVIE5jnZs387b69fTPGMGM2bOZLC/v+B7iUWjlJaVcdJpp/Grn/2MbCZTYD0g2Ur5XA6XUBu+6ppruPHHP2bN6tUqmW6dIOzCIqGyqoqv/Od/AtAudlKTYiocHsHUBBiRkc/nGRocpKurSz3A5fmL74UkCthsNpwOx5QwJenzVtM869/y306nc6RfxoIpyZq02e1Ma2yktLRUCW2GS0rw+nwqbedyuSivqKC6psbc7UlMDQzQtns327duLXCslKy2yTCVy+U46aSTpjyZwDvD1UFPeY038Fe9/jrXffvbDA4MILnUp5xxBhvWraO7s3Nkm1dXx7euu46VDz3Es08/bfK4PR5KSkoYEhRBMJlCuq5z1NFH43A4eOO110aEDYsc1DKZDG6Xizlz59K2a5ei+zqFTWrBQxEUbdcu0kPjCd6p11gHHowpsW0N2WRlEy6MuWwWfYIBY72+eCxWwJyxrqisP5MCeXJi0XUdt8hZy12L4vAbBqVlZWobHBH3Wdd1Ghob2dnaSj6fx+fz0TR9OvF4nF07d9LT3U2FMEcDs3ktXFJC1QS1M3jnFMeSkhJOPPFEnnzySQ4bR4BSM2/EM8CHDcOITOW8mqadDSw1DONbB3xRByHGw9T6t97i+9/8Jt1dXYp1dfLpp6MDHe3tKj1aXl7Ot3/wA55/+mkeffhhHA4HbrebkrIyBvv71eJEEjsOX7iQuoYGU7RV1BWKH2g54fkxfcYMeru76RF0X6fTabKjLGNUA2XkJhcrdmF+NV4ULEo1TTUQjhdOl8vU1JK1VdHEOF6oSQmTQaVPMFHJyGaz5Isw5cJMtUkPFLtQTccwCJeVqUlncGAAA3NybGxqYveuXeRzOTxeL9NnzlSL7b7eXsorKqiorATM4rnH66V+jN2JNf4WltdkuLJi6u/eKQ+wbetW/v0LX8Ap6IRg3oCH7ruPz1xxBcevWEFnRweVVVXMnD2bb/7HfyhfDkm9A5jW2GhKMqTTOOx2vnTttfR0dfHFK65gaHBQvZ+1TgDmVri8spJZc+bw0H33qTrBWDspydfXdJ3SykoG+vvN/o0JBrPc8lpXNpMxwNxuNy63e9Lipfw8VnmIia5BFt2lDprVOS8Wi6nO/kw6zb69e8lkMvh8PoaHhhjs71eryJxIEXYJuRuPx0P9tGlsFLtAm6aRzWbp7uqiVIh+Dg4McOEll9Cxbx+V1dUFMjvWOJDB39vbi9PppERowf31r3/l2muvneglZwHrpzqZiHgc+L6maT8yDGPyL+R9EHt27+aLV1yBppIOSlMAACAASURBVGmUlpaaO8NslpUPPsjHzzuPb37ve+xvb6e0rIxDWlr4/je/yZpVqwowZRgG05qa6O/tJZlKYbfZuPJLXyIRi3HFxRczYLF4KMaUruuES0qYv2gR991zj+oHGQ9TUuuqoqqKwYGBcR0dZRRjaiL8yetzezxKJmiyUGwvwxizyXiscLpcGIIVJUM6NrrdbpN2v3cvmWzW9DmKRhkeHFR+TPJ1XZ2dDA0Omg3YDQ1s3rjR1Puy2chks3R1dlJRWUkikaC/r49PnH8+3Z2dlFdWqkbK4jjQRdoB4kph6n0xodx1xx3ouq6KQGCu0gOBAPfdfTeXXHYZh4qZ8X8eeIAtmzczbdo09uzebW7DRfNeNptlyVFH0dvTw4fPOYd0JsP/3H//KIqt6goWLocer5em5mae++tfiQwPq2sYRbMVf5eVlxOLxSaVlrCGTENNxFaRIT2io9FogXfIeHnbqQx2GPk8QVHfSBV9PoegZWczGerq6znxlFP4y2OPmQ8DTOaHIYqtoVCIUDjM0mXLeGvNGoaHhtjb1obL5VK1GaeQyOns7MTn9VJVVcVD99/Pww8+iNPh4AMf+hAf+NCHRq2wD0R6pbOzkwsuuMDk6+s6H//4xzn77LMBuOWWW7jllltYv379OiAMtAGdwG3iMzcDTwKvA0cDq4A7ge8CVcB5hmG8aRiGoWna88DZwH1TurCDFOOlj//8pz+RyWQKalVOp5NwKMTKBx7gC1/6kpK7+d+//IV1a9cyrbGR9j17zFWv200ylSKZSLB46VL6ens56dRTCQWD3HnbbaMotnK363A6yedyuN1uZsycydNPPVVQW7SOVTl2Nc3sg4nF4yamJplMrJ9bNwyYwvG5bFbhtth8bkxMTXrGwpBukFb1cZlpkVmQqupqTjn9dJ5+6in6+/oUphATcSAQIFxSwlFHH83aVauIRiK079kzgildx+V0Mjw0RFdnJ26Ph+qaGh5/5BGeevxxbHY7p591Fh8655xRqsMHKmc0Hq4mw9TfXXoFYM2qVYovbQ23x0M8FlMdqclkkjtvu43I8DC7d+0yt4KGqbprdziIRqP0dHcTCAQ45YwzePKxx8jrpklOceeo9Pmw2+0EAgEGBgaIRMZetMqCc7ikhBmzZpny6+P4lADKKVKq9zqczlFOaxNFPp9XXgwFRcVxmCXWtNZkE5amaeR1fUyVX5krlytTl8tlHidWRzbbiDhkIh5n4aJFfPu//ovhoSGT8eXz4fP58Pv9+P1+kskkF332s/zw+utNwAF19fXU1tURKinh/nvvZeVDD426jkwmM+7upTjmz5/PW2+9xYYNG9i4cSPf+tZIVuryyy9nnekOeSSwD7gROAZYYznFLOAGYK7480ngWODLgLWIsxo4bkoX9T6IVW+8QUD09VjD6XaTzWbZv28fYK5cb7/lFoaHhtjV2krT9OlomunnIVV9u7q68Hg8fOTjH+fhBx8kn8uZXfNFmJKTjJTxiUaj4xJVNM3slyopKaGpuZlwaalZ/xhn/DqdToUpqcwrte2mEvl8nr6+vqljylKDnAqmdEumxBo2UYg3DIO6+no8Xu+IerdI8fn8fvyBAKlUitlz5vC9H/2IRCJBUqSzFKZ8PhLJJOeedx4/+fnPKS8vJ5fLUd/QQE1dHaVlZTzy0EPcf889o67jQFNe4+FqMkz9XaVXZIRCoTG3uJIh4vP52LJpE/9y2mmsW7uWtt27ad2+ndbt25kzZw4+wZXO53IsWryYn9x0E3fedhs7tm83Uy39/aastmXWNsREVFFVRUV1NVs3byYWjY55nXLlIX+eEEZekiVSHJIuKQHgE5LgE9yYUfdool2HXYBJil7KFf5UdiqGYZBMJFRKw/p+WdGopWkardu389jKlWQsVGIpSCk9U9r37uWOW28tYPPIz58T+eJcNsv2rVuJx2JUVFaq93O5XNTW1/PEI4+Mahp9D2QibgKeNQzjUaDMMIyo5Xe7DcN42zAMHdgEPGOYN/JtoNlyXA8wfpPH+yxC4bAq/FpD5vZ9Ph+tO3bw4TPO4I1XX2WPwNT2rVuZNXs2wVBIiRHOO+wwfvrLX3LvXXexZfNm4okEA/395kO+CFOZTIay8nKVqlECreNgyjCEPJCwrZaq1mOFxJTT4cAr9MfGjbEwNcHxdtHDIjFl9XSfLAzDIB6Pq88zClMC+227d/PYww+TTCQUyzIrMJVOp3E4HHR2dHDHrbcqeSJrLSefz2O32UhnMuzetYuhwUGqa2pG2GhOJ3UNDTz91FMqbS/jYGHqfbFD+dePf5yYhZInIzo8zLz58/F4vVx1+eUkEwnC4bCiBeu6TuuOHSxesoTDFixg2THH8NNf/IJf/fznrF+3DofwGtA0jXQqhdfjISx8EdweDxddcglzW1po3bp1lNd8MQBkQXpvWxv72ttV0XGsASd7W2QuOpFMTkx9lOfQRjppJxrIkpYojYAmG/QFuWZG2C7WNJ7D4cAn2Ca6rqsHgZws5G5L6pUZhkH73r0889e/KvXTdDptynckk3i8XmbMnMnQ4CAb1q3Db0lnypDKB/v37y/4+bvpLvf73/8eoAkzjQWQ0zTNOu6tWzXd8n+dQtKKB5i8Y+7vEGMtaj7ysY+NNKpaIhqJ0DxjBpVVVVx92WVEhoYIl5YqTBmGQev27SxYtIiFRxzBEUuW8Itbb+XOW2/ljddeM/WlBCNLPqTCJSU4nU5cbjefPP98Fi5ezPYtW1QRfzxM2ex2AsEge3bvpn3vXlN5t0jQUoYkkcidQCIen3hCKdqFTJYhkKkdt8ulJIEmimJMAQVy/ppmdr37hCy9nHRA1GF1XXmluFwupbTe1dnJ008+aerliYkmmUyaHfouF7NmzyYSibBxwwYl5jrqGjSN9r17C36ezWanvOufLCbC1PtiQvnwOeewYNEi5Z8cj8Xo6+sjGApx4aWX8t93303n/v2qK9Rhtyt3slw+b2rhxGJ88oILeOXFF9m2dSt1dXWUlpcrQNkErdXhdOLxeJjb0oLb7WbXzp0FVEYZchA6xQDzBwJEI5FxqcE2mw2H04nL5SJcUoJHXKeu65P6PVveFBi7W3essDscBdz48U9bmBYb63fSbMxKUzbE57JpGplslvqGBuobGkjE49hsNmYfcgjTGhtVis/ldtMybx6HzZ9Py7x5AMycM4dAMDjmalm+v7sot/turabWrFnD9ddfD/ApsQMB2AbMeAenmwP8f8NHPv0DH+Co5cvp6+0lMjxMXPiTuz0eLvnc53j4gQfYs2ePUoVwCEajQ1Bp2/fuJRKJ8LFPfIK31qxhw7p11NTUUFFRYY4lgY+MUK9wu93MnDWLqpoadmzbRm48TNlMH3aP14vH41FyKuOlxRzC1iFUUmJiXyxCJivaF8d4Fg/F4XA6iU8mpc8YmNI0s8dE0wowZdUXk70nduHGms1kqK2rY1pTk9qlz5w9m2mNjaY/klATl5iad/jhGIbBzFmz8Pv9BbWgoovDVYSfd8uxcTJMHfSi/FhFMI/Hw0233MLTTz3F4ytXkkwmOf7EE1m6bBm/uukmXn3pJfr7+pQZV3l5uRKHzGWzxGMxLrj0Unbv3Mntt95Kf28v24QUhdfrJZFIqPeNx2KES0o45vjj+fPddzPQ369W38VfkKHrIOiLgWBQ9ZqMFbqu43K7cbvdlJaWEolECphl1pDOh9b8rKQfT6UgKY+f1ENinNdZr0MCM5VKgUjryW7nWDRqslFiMdA0IkLxOZVKES4tpbyiAj2fp7y8nL6+PtUs6vF6icfjOB0OTj7tNLo6O3nztdfQBU1SxtDQEFVVVTQIf2wZ75aQ3c0338zAwADAc+LhthqTsXUC0HqApzsR+NrffFEHKZxOJ9f/8pc8+9e/8shDDxGLxTjmuOM47sQT+fVNN/HKiy+amBL097LyclMQNJkkl8sRjUS4+PLLGRgY4IaLL6ars5OtW7YQCoXw+f0mewmUCVsoFOKkU0/lj7ffTn9fn6pzZItqjfLf+VyO0rIyomOYXlmPtYsO+5LSUuLxOAN9fWMeK430NDC7/uXrhcz+VEI+H94RpgR70i4owmBacKSFKoBDEIBi+TwewfACc8cYGR4mmUiYBn41NWSFr3x3Z6epTajr+AMBkokEms3GmWefbXoMPfssulCIlhGNRgmGQsycNavgGg8Wpg6qOCSMeDJMJX743e/y2MqV+Px+Nm/cqG5ITtiVOl0uhgYH+fZ11/HS88/z0H33kRSA0MSKwel0mlz4nh5SqRRHH3ss6WyWPTt3sl+o8EqPEMcYg0+q7Uo65UQhC42yG1nuTIoVV2GkuTAnmsVkV/+BxFhCdtZrMQxDFTGLefdyQpOTmmzeSqZSChyaeA85wThdLlNMUtAcXYJsIJurEvE4jU1NyttlbksLXV1dZprEbicaiVBaVobb5SIyPIzD6eQr3/gGs2bPLrj24447jjVr1oxyBPwbQi2VNU2rBf5oGMapU36xplUD9xiGcfK7cC1/V0z98mc/494//pFwOMzb69erlWwum6WxuRm3x8PgwAD//tWv0rp9O3fdeSeJeFzVN3TDwGG3M2P2bPq6u0kmkxwp9J32trWxd+/eEUxhTmzFxWqFY6ERNll4PB7zpom6J4y9iy8eq1JzbKqFe3mOyTBlt9sVm61gpyRSgbL2IXtrUiL1qHpsRDYjl80qspBNpJ2dFgKPw24nFovR0NSkSEtzDz2Unq4u02RMPP9KSkvxeDxEIhFsNhvXCDVia5x++uk8+eSThEKhKd+LSWJMTL0vaMNjRTQa5dmnn1ZNcXK76HA40Gymo2B5RQWhcJiWQw/lm9deS1awT2LRqLlaEUWvhKA+xqJRFixaxIP3309CFMasmlq6rlNWXk4mkyGbzRIIBgmHw3R2dIyrOWTd9spC21QALkkB1uLkgcZEqTF5PqmMmi7i3lulV7xeL3UNDcQiERJil6EZBj6/H11cn8/v58Mf/Sh7du/mjVdfNXPAdjs+v598LkcimaSsvJzPfu5z2B0OHr7/fjo6OigrLzcZNt3dplVpXR2ZTIajjj6ak049ddwmx/Ga9f7WMAyjU9O032qaFjKm3ovSCPz7e3JBBzFSqRRPPPIIoZIS8+HlcqmUl2az0dfbS3VNDYFAgKXLl3PDj35EOp3G6/ORF5iyYU4EkeFhFh91FEODgxyzYgV/vusuYtGomTLL59XTJid2Irlcjmwmg8frpaKigs7OzjFZUcWYkpOYrEtOFBJ7gHI+PdCYCqYkwacYUw6JKZGtqG9oIJlM0imtgQWmAFLJJF6fjw/+67/S293NSy+8QF5YCof8fvL5PIlEgtLyci6+9FL8fj//88ADdOzbZ2YGdJ2+3l7QNBqmTSOdTrN46VJOOvVU6sbQv3svHRutmHpf1FDGir6eHrWldjgcLFi0yPRxFoW7aCSC3eHgxptv5onHHqOzo0OxkFyiaQ/MQdnV0cHgwACfufxyXn35ZXq6usjlcpSUlKgHqyHeK5lIqJVOOBwmm04r6ZHikKsVm2CI2Oz2CZugZA+LS9RlyoTz4ajjtdF+3e8kNM0UoouPIb0vu+Hl+w0ODDAkhOY0RujFckUoi6Viu4tDiEMahoFNMH0y6TSnnnEGb61ejcvtpqq6WuXX6xsbyefzzJ03j+/96Eec+6lPjTuZjMcGfLfCMIz7DmAywTCMVYZhrHsvr+lvianer6HBQZMuL3aUCxYtUiv6fD6vzNp+ctNNPP/00+xvbzfxls3icrtVrUPTNHq6uujt7uaiSy/llRdeoLuri0wuR0k4rBQkDASrMJlUNcby8nJSQk5kMkxJWr8xXv+W2GGXlpWZ6WaPRyk6jIXBd2vHm8vlxrSzsGLKpmmm+ZWwx5ZZENnnZYCyJh7o6wORTZDCmvLzJxIJTj/zTLZs2oSmaVTX1qp6ZV1DAxqmRtv3fvQjPnXhhWNOJmBOKMU073czJKbetxNKWXk5GUFbDAQCBEMhjjrmGOYeeig1tbUcfdxx/M8TT3DPH/7An+68U7GqZIOj1+tVW0eb3c63vv99nv3f/2Xd2rUk4nGGBgfp7enBKWm3YpsqJySPx0PHvn10dHSY4niMDdx8Po8uBlI+lyvQ2SkOwzDMVZzTSXVtrWJSjXFgQQHTSgs8kIet3AVZQ75eimKCuVqKRaOmv4MY2JqmqS5hqYr67NNPs3P7dsW7l/c7lUwqSYkvXHYZf3n0UUVgsEZZeTkvPfvslK//n/HuRigcVqZPoWCQQDDI0uXLaRGYWnzkkax86imeeOQRfnfLLeoBmRQWtF6vVz2UHU4nX/v2t1n9xhusfvNN4rEYw4ODdPf0jPRdSUyJCcnj8bB//36TYDMFTOm6bsq0jIcpcf54LIbdbqeurs4sqI/z+cfClEPszg4kiutCii0pJkAwi+DxaFRJQEl2nCzYy/T68888w9YtWwBGMCVqWZpm6t598XOf4/4//5l4LDaKXFBeWcmLzz8/pet+r3b9Be/xnr9DUUzlgajrOmgaLfPmEYtE1ACx2+2UlZdTWlrKNddeywvPPceqN94wlYkFc0JukbPZLIFAALfbzZFLl/Lic8/x6ssvF6SXDMNUy/V6vZSWlVFSWsqSpUtZcMQR6mFc7D0iawbWzyPTXXJSmSiy2SzpVIr97e0Tpsbk+0ktMesK8UCimIIs6yoerxebnKhcLtMzXqTHdF1XGkLBYFCltzxer6lbJs4TDAZpmj6dYCik7kFNXR12h4M9bW3s2LatIH8tdz0TxXgU03/G3xaS/j1/0SLi0WghpioqCJeU8KX/+A9Wv/kmLz73HI1NTebDFtTkkMlk8AcCJkvy0EN5e/16nnvmGVUjAEBgyu12U1ZWRklJCYsWL2bBwoWmpYLFlEr+bS/GlEVMUZd1kAkim82SSadp37vXxPcExBlATVJK+WGKrEoZ42HKK/ABIxOV08L0cns8TJ8xg1A4TF7sQrw+n3l/xWTj9/tpbm5WorM2m42a+nocDgfte/awdfPmAkxJ58fJ4p2k/95JvG9qKLlcjvvuuYf77r6b3t5efH4/537qUwwODJid8vKGaBqfvOACcrkc133zmwwLSmRFVRU9XV0m60GkxpIiT3n08cdz71130bl/v+p9ME9lbkHj8Then4/Kqio8Hg/9fX3K3MZ6rGEY2DD9F/L5vOla6HBgwLiOjgX9H6CKcZM2OloerLIB6h3lhMd4jWEYZITmkJwQDcM095EsF00zPeVlkT4cDuP1eCgtLaW3t1fVszwej8k+Eem80tJSwuEwqVSK3p4eGqZNUz0ogwMDnClkUf4Z733k83kefuAB7vnjH+nq7MTj9XLupz6l0lVybNg0jY987GP4fD6+dOWV9HR3E4tGqa6poWPfPoWpXC5HMpHA4/Vy6umnc9cdd7CvvV1Rea2F52Qigc/no6qyklA4TH9fn6pbGkVj2zAMfD6fSTVmZIIZT8duFKYEuWWqmJJxIGQGa1gXPNbFaUawTuVEYIhJJCF6aOR7SnHIYDCoGGw93d1KacDr89Gxfz82ux1/IECpYFRGBcsyEomovpX+vj6OW7HigD/DexUHneWVHaO4ZhgG37z2Wp5/5hnC4TDpTMYcUPE4x6xYwcfOPZe3163D5/dz9LHH8pfHHuPxlSvZunmzYnPl83k8gqEiB3dNXR1zDz2Uro4Otm7erPpNrEJ2sqs9FA5z2OGHs33bNjPXnM+rPpJidpRTSMlL6QQDxvWjFi9SuwE5iY1332XBL5fL4XG7zTTZGFapVjbKeDnj4uOLufOyz0TDfPhIhklW3H+ny6UMltyiLmWlYXosYPB6vTTPnIlb7KT2trWZNZOWFmrq6ugV3i8f/8QnKCkt5ZCWFiqrqkZdp2EYrFixgrfeemvCz3OA8X7a7hwUTIHJknzi0UcJhELqO00lEixYvJjPXHYZb61Zg9vt5uhjj+XlF1/kvnvuYce2bUqvLZfLmZgaHDQxYBhUVlcz7/DD6enuZsvGjWpRIvWv5GsxDIKhEIfNn8+u1lYl+ChrIrkiTDmcTtOvRPQ9wSQPfMlSZGT8j1dQd4peFtnHxjiYsqqGv2NMiRqQgZnCUpgS9RKX200mk8HpcOD2eBTtV+7CXC4XFVVVdHd04PZ4aJ4+XWUG2nbuJJfPM2v2bBqEEK6ez/OxT3yC8ooKZs2ZQ01t7ajrNAyD448/XkqmvFsxJqbeFzuUTW+/zcsvvEBVdTVx4ZXs8Xjw+Xw89/TTfPHLX+ZIYe6yfds2/vLYY9TW19Pf30+30BnKZrNkMhmWH3usKWOv63z03HPNnUlHhznIxUCVwoVut1vJbk+bNs3c7YgOVcMwyIqGJK/Xq3KhckVvEyke+XPz9OMMQsMo9DAQNZuxIpPJgJCv0A2DiJRQECssGdZ0gXXFZ+0tKbyEkdcGgkEMXSeZTKqcrEwz5ESXeklpKfXTprF92zaS8bja7em6jsPppKysDIfTSYVgnMSiUXYLj3JZf7Hb7QwODIBmenLnslkefvBBRUk+/ayzOOff/q1wxXmQtub/6NG6YwdPPvEElVVVJBIJ3G43Xq+XQDDIG6++yjXXXsvFl14KmHTfB//7v6mtqyMSibC/vR2PoLymUymWH3MM0UiEZDLJpz/zGe7+/e/Z196u8AAmphxOJx6fT2FKWs/Go1GMbBabqMlpmobP4yEtdupWNpfb6SSdTJKVKWaxYBwVlgUhTFwfkI6DJUJ1uViWRJ3Sgin5mWAkqzD6Ekauyx8IoGGqC+d1U6VY0unzuRxul4tgKERzczPbtm0ztboSCbxC8UOm8202G1VVVaq/bvfu3eakZGkDGBocVGQXPZ/n0YcfBsznzwknncS555//blLuDyjeF0X5115+2XQ4S6VMCQghEeBwOEDXWbt6tTr2+WeeUV9G8/TpyttArtgjolHqkiuu4MXnn6ezowO7yG8idyTiwSkbjlwuF26Ph4GBATJjNGKlBG22pLTU9POoraW0tHTUCkoW/dyip8Ma1uajqeRsI8PDDA8Ojqy6xnnQSskIMCeFiZrEFIPGUgeRIRvBdF03J+ZjjmHhokXKElmyTnx+P26Xi96eHv7jG9/gJzfdRDwWw+vzEQqFlBCngTk53nzbbXz64ouxaZrqAp42bRq1dXU88cgjvP7KK6M+T7FS6j9j4hir3rTmzTfRhRwOmG6YYPZA5HWd1W+8oY595aWXiMfjpJJJ09VR0wr6ToaGhshms1x06aW89Nxz7G9vx2634/f7zXEkrkHWDx0OBy63m0AwSF9vL3mxOpdhGAbZdBq/z0dJaSmhcJjKqioqKyvJyj4oy7HjYsoyiUxFXSIqmo0nO9bK1rI2KhaHxJRkeVrrSDCywJSyREuXL2fhkiVqwSe1w/x+Px6Ph96eHq784hf5xS23mKlFj8dsJBWY0jSNVCrFjb/6FZd9/vNomsaMWbNMTDU2UldfzzP/+7+8+Nxzk96L9yreF8iVBfBcLlfYeCPSMvKLu/O3v+WXN9xAZHiY1u3b8QcCzD7kENp27yadSpmzejzOpVdeSTQa5c1XXyUej2N3OPC43SrXKodrOpMhEAig6zqbN20yaxtChdg66OSk4hZNizEhD1O85ZXHylyoXNn4hPLuePIPcuBZdzgT5YMl1VO+Vr7vVOQldMGKsbrPyc9gtSF95cUXzX+LFWVe0B1lEdDucPDnP/3JbPJyOFTKz+PxmN+n2Nm9/sYbvPrii1RUVRU8AOSK7C+PPcbyY49VP3+3JCL+r4eUJ8kIe9yCMEYovX/+05/4yXXX0d/fz84dO/B6vcw+5BD27N5NKpUyaefRKBdfdhkOh4NXXnyRmGBVuT0enLIbXpw6k07jF5jaumULeiaDnsuZJlPW8WkYpBMJHF6v2VAbj5MUvS66YWAzDNNmWhyrF2HK6/ORSqUmbUIswMcEE4lMicnXytfI3cZEYej6mJjSdV1J6xuGweuvvGL26eRy2MUzJitUnfMixb7ywQd55qmnTGaloBfLxbDU43rzjTfYuH49peXlBTsRm81GZVUVTzz6KCecfPKoOs/BiPcFy+uwBQvI5XIFfigwor9zxJIl3Pnb3/K73/yGktJSJaiWiMfZvm0bS446ivlHHEHTjBn86YEHiEYirHzwQaVcKlNTTpfL3JqKLbbf76equhq/3086lVJ0QOmfYr1mXdRoDFAUW7mKs4b1wS4HaCqVmpD9ZS3sAUpMbryBIIEihfkmowNKSXFA9cnI1J18P4fQV5JgiEQi5ATDRvrHuD0e/H6/2moPDAww2N9PRqT9JAjcbjdzW1qoa2gg4POpQuJwkcxGIBikq6Oj4HP+c0J5d+LQww8nl8vh9/sLkt1ybC9ZupT7772XX954I8FQSO3U0+k027Zs4YglS1i4ZAmNzc3ccc89IwsIh0MpJWSEQm4gFFJY8/p8VFVXU1pSAqkUusSUUMq1tFdjGIb6rqORiKm8KzBlHdFysSNptIZhkE4mp4YpRnY51p8Xh0zfSS0+lVEY5/gCTBkjNH8rppxOp8n8Ej+LRqNkczllTjcKU4bBQH8/Q0NDijkn+72cLheHzJ1LXUMDpSUldHZ0EIvFRmHK5/czNDBQ0DQqNdoORvzdU16pVAodWHHSSUoeBUyJ+L6eHo474QQcdju3/OIXgHnDPB6PeZNF93zbrl0kEwlOPu004rEYD/73f5tNQHV1KtdqFxREj8dDMBRiWlMTJ51yiulGODw8aqKTD0i38NCWAz+XyxVQX2VRUj6M5aDSNFOduJihZX2Qjxd6Pj9m05f1PQ3DwOvzkU6lJqTiWlUAAOXf7XA4FKgxDCVmKa9T1kJs4vhcNsv0mTPNzvh83mysqq+noroal8dj0oo9HpYsXcq8ww9X+eDpM2dSW1tLOBzGbrfT29tLf38/mUyGeCxG5XNPeQAAIABJREFUZVVVwf14D2S2/89FJpMhFo9zxtln02/RWEsmk/T29LBk2TJKy8r4+U9/iqHrysdGCj0aus7O1laS8TjHHHccTpeLP915J5qmUVNbW5A+1XUdt9NJIBikqamJ0886i6DfT1xYeVvDEFRZt8gWuFwuMwVXpDCsC0wFBLXd+qAOBAI4Ac2i9G2bCFPiGF2SBcYJqRzu8/vJpNNT0tSz1k5l/VJlVQT13i4MtsAsuGuW680ICalAKGRiyuWirr6e8ooK3KLnx+FwsGTpUg6bP5/yigrsdtOXvnn6dELBIA6Hw8SUYKYmhCaYdVF2MDH1d51Q8vk869atY968efzg+uu55IorMAxDFdqv+epXqa2v55PnnENnRwed+/ezfcsWXG632cUuHtTd3d2c9cEPYhgGX/7CF9i9axeb3n6boYEBSoTsg1xpp5JJnE4nfr+f115+mc2bNpFOpcZcFUuGl2w4knaexaklwxjh3judTvyBAA3TppFIJEYVFA3D1BezrnBkFBTSprBNjQwPk0wmRwqAY4CqeKsv1YNhhKYhFQKiQrBOptSkr72s0+zZvZs9bW3E43ElY18SDqvJZ0iwgQzDoK+nh5q6OioqKzn+xBPp7e7G5/NRLXaEQ0ND7GxtZenRRxdc34E6y/0zCkPXddatW8chhxzCt6+7js9dfTU2u51uoal25Re/yNy5c/nEv/4re/fsoauzk21btmC32ykpKVHpq+6uLk4780x8gQBXX3aZwlRvby9lwthJYiqRTKrepldffpnWzZtJJpO4RB+LDPmglUZauXyemBCZpDhdK3YhPrcbr9OJ3+djWlMTOTHeCw81V/LvKqZEjDdRWcestY9GE+9jGKbvUEz0/Mh0u5y0c+Ie7G1ro23XLuLxOA6Hg7KKCgLBoMnwROxqxG6jv7+fispKaurqOOGkk+jv68Pr9VJdXU0gGCQyPMyObdtYunz5KEy9l13y1jjotGHJFzcMg3Xr1lFVVUW9RS7AMAz1UFm7ejVXXX451TU1vLVqFU7xoMlkMtTU1lJRUcHQ0BAthx7KEUuXcvcf/sC+PXuIJxLYLQ1GTc3NdHd2kkwmKRP6XxXl5ax6880Ruq+m4RZbfus9cXs8ZNJpnE6nuVWdoE4hKcWyOK6LbfpY9RBNCtmJuoRd5qInKRha6yyy8as4pHRFNpMZ87Vy5yEHoQSklN/W83mz0VHsOjJCfiMUDivpes1mw+fzqb+TySSZVIrZLS0m3TqfJyBSKZIaaYhtPpgD62ihfjswMEBVVRV1dXXs2bOHH/3oR9x///0T3gcZ7e3tfPrTn6a7uxtN07j00ku5+uqrR922KZ3s4MR7iqmNGzcSCoVoamoaeUMLprZs2sSlF15ITW0tq994w1w1a2bPUWVlJdW1tUQiEeobGjjz7LP57a9/zb72dmKiZgimZlXzjBn0dHWRSCYpCYcJlZZSW1vLm6+9hiayDIZIIaXS6YKFlcSUzeXCkcuRFT1dRhGTEYRGntNp/k7XzYlHYConbqY1jeYUbEI0DadFHmiimAqmpJhrgf6YYCuqFLLNRrAIU/K8umEoEpDH6yUrUuahcFipodtsNqXwIZmlqWSS2YccYmZjcjlC4TB2h8PMoIg6jNOyAFt85JGcdPrpDA4OUlFRQX19PQMDA1x11VU89dRTE94HGX8Lpv5uRfnW1lZ8Pl/BZAKo2gDA/ffea+Y0w2GzsJ1I4HS5cDqd9Pb0UFlVha7rfOBDH+L2W2+lfc8epf9j5bL39/Wx4sQT6ezqYsnSpbz68svs2rmTYChEf19fwbElpaXmw1FMNE6Hg2AoxGB//6iBVly8l7IOqVSqoEAnGSAFRUHDlKSQOV6r4+FEUQCOCZhf8v29Xi8Op9NcKVlZXZpGNBJRtOjq2lrisRi9PT0KKD5h9QtQXlHBipNPZuP69exsbcUhQOj3ekmnUmTSaeYdfjif++IXee3ll9n09ttUV1fjEqqvnZ2dlJWVceqZZ+J0Ojn0sMNomDZNXW93dzcbNmzgtttuG9OeeLxwOBzccMMNHHHEEUSjURYvXsypp57KoYceOuVz/P8ecnXc1taGzWajscgOwIqpB++7D5vNRigUIhwOE4lEcAnvjf6+Pqprashls3zoIx/hgXvvpX3vXrNPxLIKz+ZydHd1mZjq6GDZMcfw0gsvsGPbNkIlJUS7u83eKwSmSkpMaXzxvdocDspDIYIDAxj5PO2AHcAwyBer/WoaTrebVCKBZpl4bHY7TiAjfiafbrLfQ/ZWTWX2LiDVTNBlLycTj8eD0+UqwJRhGNgtmHK73dTU1ZFMJunp6lL3zuf3kxA2xGXl5Rx/0km0bt3Kli1bVI9aIBAgk8mQTqWYNWcOV3/5y6xdvZp1a9ZQWVWler06OzsJBIOc9cEP4nQ6mdvSwjTB0tN1nZ6eHjZt2sQdd9xR0Hg6WfwtmPq7pLw6OjqIRqPMmTNnwuN2tbaqolbLYYeh2Wyq7yOTydDT1cWyo4+mvKKCLSJ1JS2D5Q202WwMDgzQvncvp5xxBju2b2fH1q0ma0sY2FgHcCIeJxAM4vP5CAaDuDwek0c/xsNb1lk0UZPQbLYxacdy9Wiz2VQ6R9Zd5CrDynOX1N6JQtU3JgiZAiiQxmBkApN/3B4P0WiUoaGhkYYxS6+MVE/N5/NKQFKaK8mmtGw2i2azMTQ4yJpVq2gQxlvy8zQ0NDAwMEBtbS2nnXmmmkzk7+vq6li8eDHLly9n06ZNHHfccaqeNlHU1tZyxBFHABAMBmlpaRnlAPl/IXp6eujr66OlpWXCh8bOHTuU098hhx6qCCtS6r27u5v5CxcyY9Ys3t6wwWQnijqL9Ea32WxEIhH27NnDcSecQFtbm9mvlEyaLqlOp7mbEJGIxwkGAgQFppwuFxXxOE5jpI9EHm0XmEIsxGw2GzmRNcgCGSBrGKTzeXK6jtNmw+NyqXHrcbvVCt4qXW+bIqbskxSvNTH2x1IIt2LK4/EQi0ZNTyTNNOCSEjYYIxp5AINDQ2ohLBtQZYpM3r/XX3mFuvp6tTCw2WzU19cTHR6mtLSU0848k8bm5hFSgM1GTU0NixcvZsWKFezatYvly5cTGU8/0BJ/C6YO+oQyNDTEnj17mD9//qSzZVV1NWmxKgiFQixdtoxpjY243W58Ph9f/+53OXbFCr7zjW+Y/Se6TjKVIpFI4PP7lRaRw+nknHPPpXP/frZs2kQylSIWjdLb04NLFN0lWGSHq6yfJGIxUsJ4aqxmITkZ5XVdFfLGmnxkIVMCsq6+ftymLTkBjXpPS53EMIxJNY50XSchvLqBguKmeTrBPolEiEWjpj+D3Y7L6TQBLAqVdpuNnq4uVr3+Ov1CMltOlFIc0uFwsGXTJu6/915279rFhnXrRsmTOx0ONm7YMO712mw2WlpaOPXUU1m5cuUBW5a2tbXx1ltvcZRogv2/EpFIhNbWVhYsWDDpQ7O6pkbtvqVMfWNzM263G4/Xy5e/9jX+5SMf4Rtf+YrZs5HPk0qlSMTj+Hw+U/cNcxX7wQ9/mEgkwoa33iKbSJCLxRju6cHmdJppKsNAkw9QQcHN6TquZBIjkTBToHY7xeViSVVXNUzDIM/IpCNDNwxyuo4NcGkatXV1KsVaHPpUMTWJJ5GsjcixPR6mYrEYsVhMpbRdLlcBpmw204Jj9Rtv0NXRoc6txCFTKRwOBzt37OC/776btl27eHv9+lG7d4/Hw/pJVCVaWlo46qijeOKJJw7YD+VAMXXQJ5R0Os3ChQunRGP71499jGQioR7aXp+PWXPm0NjczOeuvpojFi/mlptvJhQKmQ8f8fADk9ESCAQUjXFoaIgtGzcSi0TI53Kq6ByPxfB6PJSUlOAPBPD5fNTW11NTX08qlVI1FVmYLi5uqbqE+P9EmlvyPA6nk+6uLuLx+LgMFSuLBkxTIsc76H61nlvu2lwuF4FAQFEj5apMNjzmcjnKy8uZPWcObtFX4hS+13aHQ3Xjz5g5kxmzZuFyufD5fPj9fmpqa/GK1Vnrjh0F15LP58f0wbaGZKSUCR+cqUYsFuOcc87h5z//+btpIvT/RaTTaebPnz8lMsOHzzmngMXk8XiYNXs2zTNmcOEll3DiKafwi+uvV9gBlMRQMpHA7/crTOWyWTauX09+eBhXLoeWzWLPZiEex+l24y8txRsI4PH5KK+pob6xEVsyiT2dVpOMputUO53YQPWd2DTNpLdrGi4gkM2CbrowytEs/20AmVwOm8NBX3c38VhsQoKKFVOudxFTToEpOVnZxW5ETjg50ak/e84cvD6feS0ORyGmslmapk9n1uzZOAWmvF4vdfX1ynl229atBWk86Wc0UUgq/8HA1EGvodTU1Iy7st6+bRt/vusu1q1dSzAY5OwPf5glRx3Fjq1b1SDRdZ25LS3MO/xwLrvoIrZv2WJuoSsr2dfervjq+XyeZCKB2+slFApx1x13MDQ0RF76ZovjDMMgGo3S0NhoSiR4PAwNDjLY328WFC1pF5kqcrvd5kpDN1WRfT4fqXR6zPxrseaWHDjWZkJN0woaq2RYi/mSJ38gFV1rsbH4vAnB49c0Tck6SGdHMAdh+969aoL0ignb4/GYhXnBs4+LIn0ylWL23LlUVFZit9txud309/aSSqXweDxmXUfXWbJ06YTX/E4ojtlslnPOOYfzzjuPj3zkIwf02n+EqKysHBdTu3fu5M9/+hOr33wTn8/HWf/yLxx9/PFsXLdOpU11Xadp+nSWHXMMl190EVs2b8bpcimrbTlG5Y7XI3on/nD77aQHB9GzWewCU4jCei4Ww19fj+71mpgaHibT34/D5SKfSqnuesMwcOo6zW43A9ksScMgB7h9PoKZDBW5HBFgiJHJJC8aihW7UVKPDSFxNBamxHWNiakpsL9kTIapXC6ndj0GhY6VuUyGfe3tZroMzNR8Po9PmgIKTMkifTqdZtacOZSVlyvn1aHBQSW8Ka0Ilh1zzITXfDAx9b7olAdY/eabfP3fTVO8UCjE0OAgv77pJmrq6vjpTTex6vXXTVeyI4+kfe9efn3TTQwNDmITMg+RSAR/IEA2kzEBYLOZEu02G9U1NbTt2qX4+JKxkZcDStcZ6Otj3oIF7G1rM4XsRE3A6/MVeLfruq645XlQk9N4BXIley9AIEFnnXxkrwqgemwkS8Tldhfoi8mQ3bUThdrKIyQ3xPH5fF7RNGW+Op/PEwwGiYqGxlQyaVoTiw5eh9NJT3e32dyp6+hiIs7ncmp7Hh0e5rWXX6a8spLOjg7yuRzRaJRkIkGPIFH89je/IRwOc8LJJ7PkqKNGpREPlDZsGAaf+cxnaGlp4Zprrpny6/6RYrzU8dvr1/OVq64in88TEgX42371KyoqK/nhz37GeuENtPCIIxgcHOTmn/2M3p4e1X8VjUbx+3zKgEvTNKXxVd/QwOrXX8cQisDS/kCNScMgPjDA9Hnz6O7qItbbiybYVzafDyMeRzMMDIEFl91OJWDkcuxyOPDm81TpOjlG04lsuk5evKdc5NmLHvRWTLmF1p+sW7jdblNz6x1gShJWoFA6XrcwQO2idpnNZvEFg+Typq1FWpiK5XI57MIxs6e7G5fLZe5adJ1oNIrDbiedTGJzOEglk7zy4ouUlZfT2dFBNpcjEo2SSafp6emhrKyMP95xB8FQiBUnnsiRy5aNyqIcTEy9LyaUfD7PT667Do/Ho/zIXW43Pr+fna2tdO7fz0VCyC4ajXLjj39MjRCyswl6oN3vJx6LMffQQ0kkEgwPDbHixBPp6upi44YNapUgV1poGsFg0Nz+6zql5eVEhodJp1Iq36rnciQEVS8Wjaou45ww0pKNhbJGYReyCsVh3aUYhlAcHWfgyh2RbMAcFA6JY90zQG3hxyMNyM8sr0Hmb+X/8/k8uUQCu8Oh5GQ8TidO0Tmv5/Nks1n6+/rwBwKmZawAgM/nA7GSyuZyDPT3K/qzDbMT3iVYeS7RFex0OhkYGOCOW29l88aNXHDJJQV5f7k9n2q88sor3HXXXRx++OEsXLgQgB/84AecddZZUz7HP2IYhsH1P/whdoeDsvJyAFV73LVzJzt37OCCz3wGMNPDn/30p6mqriYhHUsdDhwitXlISwupVIqhwUGOP+EE+vr62PL22+QyGZOd9f/Ye/Moue7q3vdzxpqHnidJLbk1j7Y8yLKNwX4GDMZxwJfB8AhczIVlCJfAJQ6Ei7ng8AIBYggEAuEyhZsXWExh4WAcCCZgPMiDbFmzZEk9qeea69SZ3x/n9zuqbkkeAhi/4L2Wl6VWddWpqrN/v9/e+zvInCIa4jpCbkUqh1vNJhnfp0dUI/VGg7BYRK9WUUROKb6PEgQ0MxlWt1qkHQePKKdC30cVBzgV0e5qR4OFIXoigXUWdKC06pX8tVKpdOof26DKsRikGOqfSapFqim3kxml1JJUK5b2vZqmRdW8omAIomIimYw4PK7LQltO6bpONpslm82i6zpWq4XrOCzMz0dVjlhjcrkcCdM8RSYWOl+VSoWv/e//zaO7d/Pmm25aNFJ4uhXKr5NTzwrplaNC3lpuJu2PTZgmP/rhD+OfHdi3L2ICC7+GZDKJbduxjHWr1SKXy3H1NdegalpkSKMoFIpF4NTAXJ4opOowRLbDcj4gF18pQ5LP58nl87Eo3ooVK2KtIxm+kOc2lpwG2hd2OCV1f7aTpaqqeL7Pwvx8pJslECpLHy83R/meUqnUGW+cMDzlX6/pOvliMbZJjjciMeS3bZvA97n2uut48UtfiieSRdM0dIGPdz0Pq9Hgk5/9LK/7oz+KTmLpNKl0OtoIczlajkN3Tw+f+vznMQyDkbVr6evvJ5FMUigWWbFqFffdcw+HDhxYdK1P9+a/7LLLCMOQRx99lN27d7N79+7f+80EYHx0lInx8dN634qikEqnuaMtp44ePkzLsmgKnlYqlaIlUEyqqtIQyMfnX3kl+WKRg3v3EgQBeZFTckGWyCpdSLMrqsrUyZOs1nU2eB7dQUA+DBkMQwbLZfxCAS+Xw9F1muk0qZUrWWNZ9As4vQqYnkdWUVgldPSkGGWsDKwo6ID/JFBzmSulhYVYr09aNyx9nN+2mcicOo31L/JF5mehUCAhBu/xYyDOKc91ecnLXsZLr702brXJvE6lUvhBQLVa5aN//de8+aabIofMdJpUJkNC5JTnumQyGT7zxS+SzWQ4Z/Vq+gcGYhHJFStXsvvBB9m7Z8+ia326h7RfJ6eeFRWKhMqdKTRNi0yeiCS5P/2JT7DrvvtQVZWu7m7WrFvH40eP0mo2ccVGc/Gll7Jpyxb+6iMfoSROzZroT8qFVVEUHMeho7OThYUF5ufmAGLF4/bSNwyCiCypRa6FiWSS8bGxaPawRI1U3izyhCBP9Aqchnpqx4XLP8eclDCMS3rZboDFwnXtv68okUIsbX8/W2+4Iaot2a5b+lyu6/LDH/zgVBtPtMRsYRHsex6+5/H/fPCDHD9+HE3XsSwr9rOAqK8PsPvhh2k2m3SIU7IMCZt+8P77Wd+Gb3eFfP5z8euFK73jz5BXmqrGp/bR48f51Mc/zq5770VRVTo6O1m9di3Hjx2jIVBKjXqdiy6+mAsvvpiPf+QjzM7OogmFXd00CRwnuldETnV3dVFaWKDqeWSBXKuFr6rQxgvRwpCBWo3HDAOSSZRkkqHRUVAUXE0DIeIaAGoYkvV9Vus6FaDmebhCbUITh0mH00mOcU4hADGeF+fUUk6GzJd2gI3MqTiX2qqZ9lAUhXq9HtMIYv5LGILIB8/z+PHtt8evL8nPsi0n228fu/VWpqam0ATcv11XsKOzk1Q6zcMPPECpVGKoDXovryMj7Am2nXde/POnu6H8OvGs2FDOWb0a3TBwbBtzyRu3bZtLLruME8eO8fYbb4xL8nQ6Tb1a5bFHH2XHzp2EYcjRI0f4wIc/jG3bfPXv/57e3l7KCwun2NqqSkIY3CD4E/V6neGVKxk9cSJOMh8WoUHkCb+3t5darcbC/Hx8o+m6jtKGGQfiGYlElaji1HOmRX4p2VCeCuXQcenj5GtKYUq5Kcj/2olW7c8rT2iB70d96/CUlL/UDNI1LZadaK/kpKVAX18fYRjJmQPkhGaQ67q4jhNzTzKZDNlcLuqdC6LXmULTtJjs1v59p58ECfZcPHksX7EiUuS1LJJLUEBNy2LHJZcwOTHBTTfeGLWOhSpCs9FgzyOPcMFFF2GYJocOHOA973sf2VyOz//N39DT28u88I0PfR9HUUglk3i2jaIomLpOvV5naNUq/OPH6bNtjDBEA3xNi/IAQU70fZb19jLabKLOz2O4LjZRmyup6zTblMENwAxDesOQhKoyraqYQQCi9dWOAANicqOckca8qrPklOxMSNWKQNAA2nOKs+SU/L04p4g2bV3X0Q0DS8yZYhXltpzq6e1FVRRKpRJhGJIrFFhYWIi4XY7D0LJlJJPJ2B5CumnyBDm1FFr8TAqu/s7FISEqK99w443Mz8/HC1oQBMzOzJArFLjkssv48C23MDU1hed5DC1bhuM4sb/73j17aDabbD3vPFKpFB+79VYWFhbIFwpR+0gs/hDJVOfyeYrFIoV8nmw2GyneLumXylLWED7rhmFEyCjRKpOVhCM8stsHYRI9ZQhZC0eoqMoN5mwLbPsGJtmwS0NubhCZ+mRzuSeU5ZZErEUbknCRk8mGgBIH4SmiWTtZU85aFFVlfn4+br9ls1m6urvjx9u2Tf/AADnhEGgmEmw999wYNrk0mo0GW7ZtW/Qz27afNv/kuTg9DMPgv910E6VSKbbSDYKAudlZMuk0V77whXz0wx9mYmwMz3VZtnx5fEjSNI29e/ZQr9dZv2kTvX19/MUtt0S/m8mcQgMqCqGoKPR8nlShQLKjAz2ToWNyku62BVwBdD9SHFaFlIqq63GLVQ0C1DAkFYYYrksyDMmIyj4UFbKmqnimSU5VudRxSLguSTHQN9sAKDJCWDRneaKckgfCTDZLPp9/UoklCbGXf9fFOiERkxDNbMInyCnPddE0jfn5+fg5s9ls5GQqDpbNZpN+Ia4qhWi3nndeLCuzNOq12qLqBJ7ZCuVZsaEAvOq1r+Wdf/qnKIrC7MwMc7OzXHDRRVz/6ldz85/8CT+54w5KCwtMnTzJwvw8g0ND8em8XC5H4pDA5z7zGSqVCtVKhWNCXkXVtEhLRyyEUisnnc1GxDzbjk1ygHj3XzSk830a9TqE4WlkRLngptLpuDebzeVimGJ7tfBUDaSelCWuRLyAhfn5CBH2BOJv7WgWydfRJWtXXL/dakUnqTDC1AdBENuyyk2sUa9jWRbVSiUmavUPDsZtBQkgcF2XyYkJNm3ZwiMPP8ymrVsZHxuLUXa+7zM5Ps7A4CBbxdBPxjN58/9nibMdUK657jr+7AMfiKWK5mZn2XbeedzwhjfwgT/7M374/e8zPz/P1MmTzM7MMLRsWSyVU6/XuepFLyKVSvG5T3+a+bk56rUajz/+OJlcLpYTUpWIBa5oGkoiQapQoGjbhJaFq6oEmrbILVUXQ21DqBrP1mqoQcA6UcWo4j/D98l6HsV0moyiYKsqc5kMfYrCCt/HFveuCiR8n6SmkeRUlXKmZu+T5ZSiKFiWxfzc3JPnlO/Hh9Ck8CtpN9mSOdVsNKJ8M4xTCFGBkvTE59xsNqmJKlFRlMg7SHxuZZFTnucxMTHBxs2beeThh9mybRuTY2Onqp8gYGpykq6uLs6/8MJF1/pMqg0/K1peEH2Zf3j99bzsuuuYm50lnU7jeR43vOIVdHR2RpIObXOQ+bk5NmzaRBAEzM/Pk8nlGDtxgmq5TKlUWiQO2dPbS7PRwBFleS6bZeXICA/t2hVrVckyVFYeMgzTpGVZ0b8JHL7sa7Y/LgiCyHNFWHM2Gw08z8NMJKIStK1CkN7a0j8EotOMPLU8ES4+bpuFi/1SZAXQ3hKDxa0vVZTDqqZFHINGg0qlEj9OVh6u4+B7HoaQJW82mwS+fwrOrGksLCzw0zvvRNO0iIdjWdi2zdTJk9RrNfwgYP9jj7F/7974vZdKJcL5eQC2X3gh17/qVadVI67rPmPKqP/ZQ1EUrr7mGl549dXMzszEwoOv+cM/PKUkIdrArusyPTXF5q1bCcMw0vUaGOAH3/8+rUaDysJCTITVDYOu7m7sNuJvKp1m7fr17LrvPlY3GjhhxIwPFIWURBgSbQBJw8BptaioKqEassGzSAQ+vqahy8pAUVDCEMO2UXWdhUSCzlaLrOPgmSa640SwY/E+dd8noWlRq8y2I4SYaUIYniaHdKbPSc5P2nN6aU5J6P6inFLVSOhSVens6sK2bUoLC/Hj2omNvh95zOcLhWjm4vu44vPTNI1apcLPfvITNE0jlU7j2DaO4zB18mS0frkuhw4e5MihQ3FbvVqpsCBy6tzt27n+1a8ms8RXynGcp01q/I/GM76hPJnciq7r9A8MAPD9b38bV5SFnd3dzM/NYQhIa6vVolGvYzsO519wAb+46y5OHDsWeaKbZoROEovj3MwMV7/sZZw4doz1mzZx7OhRDuzdS09PD6OWFfdafUEykk5wYRhit1oUhCLoUpLjmRwbNV2nIRbgMAxxbJuEmDO0w3ZDOOWnrWkoPLmN6dIh4tnYwG5be0lWTO1Kyb7vMz01FQ/mNU0jIwTp6sI1T1VVHMeJZflXrFjB1u3bufeXv4y+B9HOk0rDruvy1ne8g42bNvEPX/kKnZ2dpDOZ+DqmTp5kYGCAt/zxH5NOpRb9W3s81/L6zYemaXFO3fmjH9FqtSh2dNDZ1cXM9DQQtchs26ZarRIGAWvXr+e+u+9mcnQ0OuEKhVxVVaOcmp3lxS97GWOjo2zYuJFjx47x2COP0NPTgyrVc0VOtVIpUoL4q4Qhnm0TFAs8r8fith5EmErCAAAgAElEQVSLL+wCLQlO6FNBRQlOtcrUMGRC1+loNukX8GHNccgYBr2+z0nfRw8jhQyN6CSfUCKyo6oo2G0E5jPF08op5ZROVjsaFKKcmpmeJgzD2M4hm8vhum5UfYjr8cTfTdNkcPlytl94IQ/cfz/TJ0/GLTK5BlmtFm9+61vZccklEdckl1uEhJ2dmSFfKPDO97yHVCp12kYi4/ey5XWmGBsdjVBelsWGjRtj5rpcmGdnZzF0nVfecAMP3HtvREj0PFLpdDwwUxSFlm1z9PBhLtq5k4X5eR564AFqtRq1Wi1mnMo2lm3b9PT2YiYS0UKby0WwvyX8kqUnEF3Xo3aYHDq2Dcdt244hgrKykbetNP6yHSeWuz9bS6x989J1/UlbZ7L1lkylFg0s5SYGxKS3ru7uCPJpGLH5lkyMVqsVnY7276deq8Xlu+u6kQOd8Hn46Y9/zN9+6lMcO3r0NNOvvv7+yLRJ6KydLZ4z2PrtxuTEBAqRgd3a9etJJpMRmkhIC83PzRECb7jxRu69+27m5+ZwXZeUIDTKnHIch8MHD3LueefRqNV46P77qdVqVKtVmpkMqoTdA03HQenpwU2lqGkae7JZnr/C5fXLXJo+uAEEISRV6EmEOKqKpSjYuh5xTzwPD7DCkGPA3jDksOOQU1W6VJVQVfEkPwRQdJ1cGJKwbULfRyNqsZ0p2nNK0/Unro4FMiybzZ4GdEA5pSrueR6FYpHO7u640td1fdFcVXK3Dh08SLlUis0CPeHoKIEuP//Zz/jsbbdx6MCB0w6c3T09TE5MUC6Xz7qZwDPrMfSs3lD6BwdptWly7di5k2XLl8czi52XXsqf3HwzH/uLv2Bubi5ibYsev5SH0DQNQ9e5/IormJ+fZ2J8PB64S50wU2DcJXtVyoVkslkCIYB4Jh2v9k1Fan25wkBnacgTjmSmG0KWP4YItz2nrFyeqJrzff8p9YSl8COwiEAo+78hkanQ2OhoPCTUDSOa/7Th9SvlMuVyGUsAE3RdJynIVRIC7InkcRyH3Q8+SL1eX3QtmqbF5fnZ4rkZym83+vr7sR0nyqlUKhaHDMRhaPuFF3Lz+9/PJz/6UaZOnowOC7ZNpVLBTCTICOKdpmnsvOQSWq0Wjx87huL7dGoapmUx5fsEiQQJRYnEH00Tz7bRDIPpbJZVus91xQbVVoBmmHSkwAsjdKWqhGS0CG4b+j5uEGB5Hug6R4F5IohwAxh1XRRgk6axQlHwTZNUoUAqCECw6UHkqehYnA0dBVHbW875zhaqqkaHUaHa255T7fJI1UqF8RMnIvCQGtlaSF6KzKlqpUKlXMZqNuM2eCKZjLXTUul0DG5xXZfdDz1EVbSogbgKfLKceiar/mfthhII9rpko0M0/NqwaRNbzz2Xl157LR/66Ef57G234Xke/QMDMVJCVVWajQaarsdCdgcPHGD3Aw9QLpfxxIC5JQbRiqJEidIGuevt64vbanLjWOrNLBf8RfIqEKsTn/Y4P1JOTYjStlwux2CA9se3n5jkz6U+lnz9J2uPwWItsLg/LCqgTJuQndxA5JzIdV2WDQ8ztHx5dA1CViMrpMdVNbIRWDkyEhMaNbHBZMXAVlVVThw7tuj1fd+no6PjCa/5OcfG316EYUhWIBsl8iuRSLBuwwbO3b6dK1/4Qj7+6U/zd5/5DC3LYnBoKD7YyAOY5GL19PYyNjrKrnvvJV0qsclxGGg0WNZsstqyOBmG1LNZTMMgsG2qQYA3MEC3bbNJbRD6AWoQYrouO4ajxdYPABRSOrEYZEVVOacAtttCUaMFVAN0JfJbqQcBbhhimSadgF4uoyuRDL0hFnvJCZEqyTKnJI1ADt9/nZwy2nMqDOMqX7bFHQEBXrZ8eewuqQuvJcM04/nmylWryIiugiSI5vP5SP5I1zl29Ohp32nxWZRTz5qhPMDC/Dzf+da3+OmPf0ylUuGinTt5zetfzw++8x0mJybiaqCnt5cdl1zCG1/zGg7u3x8zWTWB5oLo5mi1WhSKRarVKgf27qUqTusxz0NEy7Lo6uoiyGTIFwqkkkkOHzoU3RzixN0+uJPcC/k6mqZhmuYi69ClpEUgusHEXEeyfeWAX23jvcjfaRewlO0nt21Q+ESDRiAuwRc9TjyvlJJRlMihsR1iGYYhJcHSl4NJwzCiGUwYoopBPGHIvJCGCH2f4VWryOVyHDpwAE3XmRNoGUVRmJmeptjRwfzcHIqisGzFijNWYM9VKE8/nqiSrZTLfP873+HHt99OqVTi3PPP57VveAPf/da3ODkxEXEjVJWOjg6uuOoq3vTa1/Lo7t1xTult91AYRFbRnUKm6MDBgxgLC4S1GoG4fwNACUN6bZt6dzeHBX8in07jHjpEoCg4mgmKHRMFV+d87DUmdz/u4gXg+AGoGkGnyce3WszZ8MEHoKiFWJ5Cw1UgCGNI8RiQd10yQCUIUIOApOCTGI6DG31IKKLqjnPKNKN8fBo5JSuSpTkVBMHinBKfV9AGoCkvLFBaWIhfzzRNPEnqDkNyYj6yMDcXC16uOuccenp72fvYY5HfULkcH2znZmdJCj7eiWPHFvmhtMd/amLj2W7++bk53nnTTcxMTcXw2/vuvpt7776bW269FcdxKC0sMLRsGaVSiS9/8YskU6lIWE20W3Sx6Ep+hWEY1KpVLtixg4cfeICKIOTJoZq8KXzfZ25ujvPOP5/pkyc5cfx4xGAVJ4/2DUhyREKI+6OarmMLhv3STaH9hCdJUBJVI886UjdIVipyXiM1ixAIsnYuyVMNaUPaLi4p50vxZ6HrFHI5VLHwK6oaoeREImiC9GgYBhs2b2Z8dDRC+LRaEIZYAgX3kzvuwDRNOru7qVYqOLbNxPg4VrPJ7MwMI2vW8DXhHrd6zRre+OY3ky8UFl3vczOU31xUKxXe9ba3MT42RiqTQVFVHt61iwfvv5/33nILmhp5cvQPDOC4Lp//9KcxEwkSiUSUU76PJjyFVEUh1HVM06RaqbBt+3YOPPYYneUyLhCKnAJxcvd9ErOzLN++nfmFBab372fAcbAVhUNVHRcNUwuwveg+3zKYYGUX2A2PTx03mFAMvnaRhYnG4YovnhfSejQpqToKgchLzfdRk0lcx8EG6oAbBOiOQ1rTcBQFy/PifFIUBSUIInBAG1LsqUR7Li+lBMT5pUb22al8noRpcnJyMgIJtH0+EuVpmCbrNm5kYnQ0sssQ3Ri71UJRVX7+s59h6JHXfK1axbFtTk5MYNs2M9PTrBoZ4etf+QphELBi5Ure9Ja30LEE0fV7CRv+1j/+IzPT03T39FCr1+nq7kZRFMZOnODb3/wmt/3t3wLR4v/2N7+Znt7eaJEk+oJkTz9fKKAoCuWFBVYMD9PZ1cXePXtiyZX2eUehWMRz3XiQ32w20drnJKJUlYzjQKBJbNuOFlgBz7UFUEDT9cjXYcmmIl+zfQOTQ7j28H0/9ibRdZ1sLhfxQ9oYw/I5YTFbd2lI1Jrv+ySSSZJCHgWIqzi5AdZrtWhTEMN62fZSxCKhANPT07GsdjqdJpvN0i8MjY4ePRpDSR3HYWZ6msD3ufJFL+Kyyy/njh/+kOFVq+gS8ithGHL82DG++qUv8Y53v3tRMruu+xzK6zcU//zd7zI2NkZvby/VapXuri4UVWVyYoJ/+od/4Itf+xoQfR/vetvb6OjqQmtDLupCfDCTyWDqOvPz8wytWEFXdzdHDxzAt220MCRQ1ajKDQJyhQKB7+M5DvlEgpIgSyY1lU1ZKBghs47D104kuWnEIa35OKEGvk0upXFPS2dbt8fHVloMJQJmXY3hgkoYBvgiCVJ6SNUTAIEwxARC18UyDOZ9P5JrAWzA9n0ymkYnYGkahkBeWfKAJaoD2jaWp5RTiQSGaS7igcjHSNXgZrMZbSSqGvFOXDdqces6CjA7O0vLsmJl9O5cjr6BAdLpNAcPHMCUOeW6zM7O4rkuz3vBC3jh1Vdzxw9/yLLlyyMSpPgOpyYn+dLnP8//eN/7Fs12nkk5o2fNDOWnd95JR7FItVajKDYFgGJnJ4889FC8GJZLJaqVCoZhkMvl6OnpiT2apf/BsuXL2XHppVx+xRWRQ2OjQUE8Z7sHQsuy0IWqrmVZHDt6lFqlgis2GSn+1hS/LzcBRVVJZzLxzRVDB0U5vVSSvb3tJG/Ksw3Upd6QqmnxnEc3jJhXI57kjL8rGf3yNWXYrVb8+WmaRrFYjE1+ZBWlis/Gc110w+CCHTs4/8IL4wTTRVsvmUxSWljASCTYsXPnIvG8MAgwdJ20qDCTySR1kVSdbVpeiqIwMDDA0cOHGR8dXfQenkmZiP9McaYT9p0/+hGFfJ5KtRrd/+IeKhSLHD50KCaiNhqN2JoglU7TPzh4Kqd0Hce2GVqxgvMvuoirr7mGo4cOUa/VyBWLMR9C5pRtWWhintC0bY4cOUKvXeGLK23evczjxj6PP1vu8+pkk6+PFXiokqBu+xxpKHx1JstIOuC6HpcuM8qpXsNnXQGuHNZxfERVr6ASxHORvKKQCgImWy1UIn/6dimWhu8TSEmVRoNWoxEplAtkpvgAz/i5yipiaTvMtu14M1E1jY6Ojhi9GG9Mosr3PA9d09h+0UXs2Lkz1ljTxXOnUikqQnrl0uc9j3Q2S1LmlGh3Z8RMxTCMGCTULTYT+f339fczPjbG40vmLM9kTv1OKpQz9Sodx8H1PDqKxbg0BGIpA8/zqFarfOVLX+K+e+6JyT8jq1ejqioz09MxOmtgcJCRNWv43re+xczMDKaQTDdMcxFxMAgi7+dKuRxD+2ZnZ8/IManVauQLBTzXJZ3JUCqV4gF7++YQiJMdnBrghRCr+y71NVn0WYjTUhiGMeY/FH1f2Q6TpMYzfaZy2Ne+yS19DdM0qVWraKJ9ISs3ubH44hofuP/+eH4iddYksVFRVaYmJ3nk4Yc5sH9/9NkJwilA/8AA3T09zExPs37jRlzPY3JyknwuRyabjRNKES2X5cPDi+6D51pev5lwHYd6o0FHR8cir3S5+XieR7PZ5Gtf+hL333tvfAgYWb0aBZicnMSxbQxxUt68dSvf/da3mJ6cjNj3s7MkTJOC6+LKQ1UQkEqlaJRKlBIJsrrGX2ZnySoBjUCFICRUoMtQuJ4KH57oYKFlMpXL8YFlJfTQp0SSXr9FyoggxQkt5C0bFQhV/m08QkX6YURcPCcMGXZdHg/DeDPxFQVfVhxEc5ZGGGI6TiRSiSAyC/BIIOSHgFjkUnxQUU4JzglnyduEaVKr1eLNJ15j5DoTRqz4hx94ILY0Nk0Tx7Zj51hFUZifm2P3Qw+xZ/fuOKfketHb309ffz9zgioh1SgkN0Vt2xDn5+ZYvWZN/HfHcX6/UF5hGHLOyAhOq3Wa9Hu9Xmd41SpM0+Q973gHP/rBD+jq7o6JRY89+mhs2DQwOMj7PvhBrnrxi/nJHXfQIZ3OBEJCFdWDoigxl6K0sEBXdze6rsfS1mFb+SoXdQn7DYE5UapK0xz5ZYVhGH+x8nVVVSWdSsXqp2d678g5i7jZNVWNPoe2ob68sZZ+PnIjkEgSeXpZemKVf5coNtdxIrCBQJuk0umYrxKIlpzcPBzbptjRwbLlyzGFFXAikSCbyUT/z+VoWRabt27lop07WTUyQkJ8JoNDQ+SyWfr7+vB8n8nJSUqlEp5oAWaWWBY8R2z8zcXI2rXYrdZpgqtWo0F3Tw8dnZ28793v5rvf/GZESBSL6949e0gkk1x51VX0Dw7yrptv5hWvfCU/+uEPKRSL8b1tGAaTioIrEFVJXUcLQ6rz85gdHaxOJPhTv8Sy0EP3wsjiVyC1Gl5IDo/1aZ8FXSdVmmGdYVGyfELbpuynUFSAkACVLgNeudbgCy/QeNtmlSCVZrmiMBxGz+spp9xMtTBEE1W3Imaliqqim2bU0gO8MMQWxMT2XJM5LgfljvBzCZ8kp2T+SeV0XddJplKk2nJKctVURcGxbfKFwqKcSiaTpNNpEgIt2bIs1m/cyEU7d3LO6tVxXvQPDJDN5egfGCAIQ05OTrIgOHgA2SWclGdyhvKs2FCOHz/OS6+7jkwmE2tThWEYGV7ZNi+59lr+8kMf4hd33UWz2aSruzvSHBIn972PPcb01BTrNm7kxLFjfETIqk9OTJDJZGLlULlB5ItFNFUlkUzSNzBAtVajISRY4BQcUFFVTNOMnRM9UTWYogSWN5QfBCQE8QuIv1hV01BUNSIG2nbM3zitPRGesgOGU5XTmSqRdqn/dDpNV3f3on5p+3sA4uFfO/xYXoNEeElpCDlch6jvqgm5Cc+PDINmpqepVirUBBY+m8/T3dMTS9rMzszErz83M8Olz3se27ZvjyTJXZeOYpGhwUF0w+DokSOEYRj3gOP357pPuTx/05veRG9vL5s3b35Kj/99irGxMa584QspdHQwNzsb851q1Sr1RoOX/eEf8om//Evu/NGPqDcaFDs7SQpnQ90wOLR/P5MTE6xeu5b5+Xlu+fM/59jjjzMxNhYJkopDTqAoHFIUSoUCDV2nlUjQ6OtjeavF6mqVjkS0Vqsh5ILIWdE3TTxdx0iYpPToYJFOJdAU6DcUBo1oOF8NUiR0FUMFNfToTICpq8y5Kl/e2eL5CZvuhEciqdEFsV8KRAx7PzwlpaIHAelEIvpZ2+NCohO8bJGl0mk6OzufUk7Jx8icgjbAi6Lge96pnBI5oGpa5ODo+zFYpVIuUxMIsUw2S//AQGyjIZUMIDrIXnzJJWzZti1SNmi1KBYKDA4OYiYSHDt2DNtx6O3vX3Tdz2RO/c43lLm5OWZnZ7nqRS/i45/5DGvWrmVmaoqZqSl6+/p43RvewP/79a/z/e98B0c4mB09fJh8ocCK4WFy+TypdJqrXvximo0G01NTtGw7Oj2bZtQrFe2xXD4f2QIrCt09PWzYtIlatRpxVuQAWmw67YutLtRVG40GLeHQKFEeQRDg2Ha0CQj2rKZppDOZaJ4RLtYHkgP506Jtg5I8mrPZkcqqxHXdeLHQdf2Mgnjt1wmRsvPSOY+cC8mNLZFIxJ+vQtQmC4MAU+DlHcdBVRRGR0fpHxzEDyJv65Zt43teZKNcKnHvr37Fh97/fjq7upgYH2d8bCxKoFKJnp4e3vL2tzM2NsZDDz0UtxqfjpbXG9/4Ru64446n9NjfpyiVSkxMTPCCK6/kts9+ls1btjAzPR1Dt//oTW/in7/9bb79T/+EbduUSyWOHDpEJpNhpYB+p9JpLr/iCnzfj33QM5lMdA/oOpqqxjmVSKcpqyqlzk6S27ZhNBpkajUsVaUe6gREhMIAhaQfWWArpkHTdZmoNGi2LK4uOJyjBJyj+azSAzZpDmrZ40gtRcOB8abG/5lIc9AyefGKENeOEGIJHQaSASuzBp1EMxbZ6kL8WSWq+muWFZEkzxCByClP6AQ+lZyS+ZlIJlEUZdF9G4icCsMwbnHl83mGh4fjxwZh5N4qc0pRFMbHx+nt7UUhOjxKIvGEyJ3HHnmE9//pn1IoFjk5MRHnVHlhgY6ODt723/8709PTPPjgg0xPT8cag0+16v91c+p3ivJqNpscPHiQCy64AFVVWbd+PX/9t39LtVKJPM7zed54ww1ohkE+n8dqNGJS4PTUFBs2baKnt5e52Vlsx8G2LB4/epRyqUS5VEJRIq+GVDod+Ug3m/jC0nfFihU8/vjj1KrVaFOQcwSxsIKAGKqnzLYkXNAWYnDtKBHXcTAMg0QiEdl8isG7Lk4y7bMTqU/W7pioQMwyX7qRtPdr2/kwEq0lqxtVUSKGcbDYEjg+tWkazWaTbDZLoVhkfGzs1HNJxr6ixItMGARkhSKz7/uRXISo0sqVCvf/6leRva+Amrq2zeTEBA3RUukWbZRpYQ9wyfOeF3FQli9n23nnxUNMy7IYHx/nc5/7HJqmUa/XKSyBE58pLr/8co4fP/4fuvf+s0UMNGm12LdvH9u3b0fTNFaNjPCxT32KarWK57oUOzp46xvfGKEcCwUq5XKcUzMzM3R2d7N67VpmZmaig029Tq1cpiQWWVVRSKRSJNJp0sI/xXNdMh0dDJ9zDseOHmVzpYIncmpvOeDqPhVDA8cPURWVhKIShj5uqHCfZXDNqiyvK84x76l0m1EFEQCFwEVBZ7yR5JP7EmxLB2zvbTHlaKimCnj4QQgqFDWXi3WNh/2Ak225kjQMQsfB9v2ozYrYbM6QU8HTyCkZqqpGFsfZLB2dnUyMjcUHuFi7Txz+ymLwnslmaQm9v5ZlxTlVq1a5/1e/imyzhZNs4PtMjo1RF52Zru5uNF1n+uRJ/CBg56WXomoaA0NDnHveebHWV6vVYnx8nC9/+cs4jkOz2XxGcup3VqF4nscjjzzC5s2bT+vv5QsFOjo7OXzwIJVyOerBi35hzO0gGj7Zto2m6zQbDaampti/dy+5XC7CzqsqlmVRq1bZsm0b69at47/ccAN9/f0cPnSIXC4Xs2TloLi9SpFfuFxYbduOceKSkCh1u2Qym4lEJEldrxOKAXdSeKkDsUS1hBNCNG9JpVLxKWlpyBu5vQxPJpOxqKV8TBAEZ+71iv8HQiuoUq0yPjYWES0FjFGKbkreSeD7UXurXieRTHLu9u1ccNFFMRvYNM2419toNLCaTV58zTUMDA6Sy+UYWb06glYaBv2iJJ+ZnuaVN9zAzssuW6TnlUqlWLNmDe94xzuo1Wo8//nP56677voP3FW/3+H7Prt372bjxo2klmhN5fN5Oru6OH7sGNNTU+TyefpEayRG+qkqszMzUWtGiQi4s9PT7H30UbLZbPQYTcNutahVq6zduJHVGzbw8htuoH9oiEMHDpDOZOKZjaIoWL7CtydUdBWyhkpSDTBdCyX0uWPT5Xzq+5/kliuW0TPYS7qvl1JoEqKghSEhIRnb5u7DCdbO1rkgV0f3Arpcj2XpBEP9Jq4HqBqKqpA3VM5TVV4CrNR1epNJDHFIbM8ISTWQa4QMWWk8lZySIauVmswp8Zy6yCe5WWttEP16rUYimWTLuedy4Y4d5AqFWAnDbJtJ1ut1XvSSl7BseJhMOs2adetIJJOxgG4mk+HE8eP8l9e8hssuv3yRcGQymWT16tW8/e1vp1arcc0113D77bf/WvfXU4nfWYWyZ88ehoeHn3DXbDQacSXQ09tLZ1cX83NzUT9QUWLVzte/6U384Hvf4+jhw/HMIyVUcCGqCI4eOcIVV13FkUOHOLBvXyTIKBAsXpsSsKYodPX1UatWsSyLfD6PoijUBMO+vXoIgiA6mXveItRGPpeLVFvFRmBZFmYiEZ9c5NBfEix9oRcmE1tKZS+NRa0zwXs5E7mqvddrJhIkEwmq1eopjTDx2lJ7K18oUCwWmZ6aigezQRhiCE5KvVZjZnqaifHxCEknXC8dx4m1noIw5PEjR3jk4YeRxK0169bFidjd08NjjzyCZVmnLXYyOjs7SafT7Nq166ztvufizBGGIXv37mVoaOgJ5W2ajUaEolQUOru66OntZWZ6Op4L1ut15mZnedXrXscv7rqLwwcPxuTddCYTDajF/OTo4cNc8aIXMXbiBAf27YtBKildZ1jTaAWRYOPhuso3St2s1uvkvQY/M3IEV/1f/N/vfC2Ncg1t+iSuqpJOmiRT/cxMzuE7NqphkPVdqLTw8nk8rcJkTWG0BCnNYsvKJKWqh2UFKGEAPiQUlaqmsVKoa+8KQ5KAp+uxVbectYQA4hoBQjHjbPcPkp9t+6EukUySTCapVaux6ZWE3suWbaFQIF8oRHNF0T4PwzDmedVqNeZmZ5mdnqbZaJBMJnGE93ytWsVqtQjDkCNHjnD4wAFs20ZRVdZv2BBvgp1dXTx+9CjVSoVCsXjG7zufz5NMJtm1a9ciFfLfVvxONpQjR46QTqcZHBx8wsctX7EiOgGIauD8Cy/k+LFjjB4/TrPZZP2GDbz+xhv55je+weNHjsRIDamI29nZSavVwvd9zlm9mqNHjlDs7ETXdRxRvgcQm97ouo4fBFTKZUzTpKurK9b+kie49pBlsSZObYGYAfjSD1tsAPIUYwpsud1qRd4Uuh6VvuL9yc2hnSsj47S/w5MK2SEeI/XK2p+jvf3WaDSo1Wr4Qm3ANM24jRVvdALHL2Xts7kcmwRrPpPJUBVieQlxypoYH6e7pyfmnyiKclakW/vnCYuHnM/FU4vjx4+jaRrLl/iML41ly5fHwp+KonDu9u2MnTjB8WPHopw65xxufOtb+fY3v8nhAwfi32vUami6TkdnJ7aYlQ2vWsWJ48fJ5XIkEwkC1yWlKIyHITOaRr+i4GgaXhgyM1ummUiw3+xmqtTkb/7oZdjzZVq2h5svkqgs4Lk+uqGTzqYoOR6hbZMn4KStYQd1fnpAwfMC/Mj5F+Vwk83rEmQSKjNTFgcWUpRUneFmEzcIOKlGApMGYASRN4snhvKweIgvoyUW8rOFoqpPmFNS6aLZaFCv1fB8P+JwiWo9mUzGorRALP/iOA6ZbJaNmzdzcnKSVDpNrVolFCTKRCLB9MmT9PT0nAZkeSoaZBIS/tuOZ7zlJYe+a9euPetjXNelVqvR09vLzssuY2ZqKl64zxkZYeu55/L8K6/kvR/8IF/+u7/j8MGDsYCa1CaSC6UhWjN79+zh8IED7LrnnrhFU65UaNTrBEFANpeLOBiuSyqdptjREcMF5XVLhmw7uiOWohewX11Yc8anobabzhXzEdM0UVSVhrjh0plMfJJvv0Hb+TjyZpXD9DPZf8qQzyU3Kvn7kl8jy/H29yFZ8vKUaRhGpBwg2gXSWCwQbH5VsHtdISWjEJXZQ8uXYzUamIkEJycn42sqLSywamTkSQfu8n0+F08vdF1nw4YNZ/13z/Oo1Wrk8nmuuOoqZmdm4pwaXrWKbeedx6WXX84tf/EX/OPXv86BffvI5HLRYK4sJEkAACAASURBVFogHj3Po16vxy3aQwcOcGj/fg7v2sXKapVzWi0GKhVGajV2+T4Hs1mUMCTpunjJJMc6O/GbTdavGsBMGHiWTSrwKV94BWooFkVFIZ1NgwI5TeHgvEaqb5AwlaLc8NBUyCQUErqCqoQ8etihJ+Nh5UxWoLKtXifp+yQzGUzZkhY5ZQKmqsaLXkjU6pa8sScy4opzSgJYWJJTom0sc1bOX1Ul4na1LCvW+5OgH0tQD4LwlDV4OpM5NURXFFKZDEPLl9NsNkkkk0xOTMTXVK1W6R8YeEI7CHmdz1T8TrS8VojKY2k0Gg2+8dWv8uPbb8dxHIodHVx9zTVcePHFHNy3LzrFBwHLly+nu6eH2z72sagkN83YJS2ZTEbPLREYpkm9XqdnxQoq5TIT4+MRw1bcBEAMPx4aGiKby1Gv1zk5MRENnIWcvZRPcF03ckQULHoZCdPEtu3YARKIb5yY4Cj6toHYXKQKqus48Sm/vX8rcfS+78cckXahyrPFaTMXARwAsckEAS3Bt4k3FV3HFdpcsu0WtrUDhoeH45ZHKpWKFQQUJfJLkQtOsaOD8dFRWs0mdipFGEY4eXkCft+7383qtWt5yctexvCqVU/n1jktbrjhBu666y7m5uZYtmwZH/rQh7jxxht/ref8/2ssF7YOS8OyLP7pG9/g9n/+Z1qtFvl8nhdfcw0XX3opB/buBaL7pW9ggGXLlvE3n/wkjz36KLphxDkl5wC+EAdNpVJUKxVGVqzALpUojI/jhiGuEqn8qopCn+cxpqocHRoik89j1WoMT06i6Dq+oglIvUYYBlRGNpGcPE5x7wOojoXieiRDj+NanvSffZRXr1nPF264gY6ODpxalaBZxlS9aNDvhfz4YehshnTiMB8EpAHDcTDSafZVqwSKEikQi/s9IXJST6VQAatNoPFsWXWmOaYMSTGwBdpT5r2maRFpEeJ5Zdj2GstXrKBYLNIQYKMgCGjU6xGCU0CZQ3HYlTIvUqxzenqax48cYWhoiD9/z3s4Z2SEq6+5hpE2QmP7dT/V+HVzSnmSF/ytbG0Sdrv0Z+9797vZJ5wUTdOMuA8zM1zxwhfy1re9jcmJCbLZLGNjY/zdZz5DoVDg3/71Xyl2dKAokbCh3N1VNVLrVDWNc1avpl6rceTQobiPKR3UwjDE9TwMXeeCHTuYmZ7m5MRE7GPQrvgbhpFmmJR7b1oWpphBSE+CpbMPKc4oZyVBGNLV1RVL48Mp2Xtd03DbiJWyGvF9f5HwpFRVDp+k1FU1DVNUBI7jnCqzhYSMPF0lkkl6enuZFZ+fput0d3dTr9fjaiWdTseEqtETJ7CaTVavXcuJY8fizzxfKJzarDWNbC5HR2cnszMzLFuxgtVr16KqKgvz87Qsi7e9852sGhmJrzcMQ57//Ofz8MMP/0duqyeKZ1PJ81vJKW/JAQeihe6W976Xhx54IM4py7KYmZnhop07ec9738vY6Chpwf/61F/9FR1dXdz5L/9CsaMDVVWZn53FajZJplLRAuk4aLrO8MqVOK6Lt38/xSDAF3JBMqcCcUjTd+xgZnYWd2KC1baNLQ5mf3n7p8kUMjSqDUxNwzcMtPkpeo/v4967HuRHx5q84dNfo6+ri+lf/Yrv/a//hS4htvU6zZkZLF0j8HzyQcBgVxdNx6FUqzEDWKpKOgyp6jp7PA9P3Pu2yClZaXhBgAdxd+PJ2kdxTilKTBeAM+RUIkFXTw+l+XksIUfT1dVFy7JoNpvohkEqlWJgcJB8sRi18RsNRtasYXx0NJ67FovFaKNThER+JkN3by8zU1P0Dw2xbv16NE2jXCpRr9V4y9vfzrq2SjUMQy6//HJ27979m7vZojhjTv3OeSgyHtq1i/379kUkHUHCSafT9PX385M77kBVVbZs28aqkRF+cscd5AsFmpZFNpeLJUJy+Xyk79XbSxgEdPf0sHnr1pg4JNspUrMqKWwz8/k8ZiLBiePHI92cXA7pgd2O+pDsfCkGmcvl8H0/Em4TrNuliDW5GbQnfLVaXaS55YsBout5pAVPJJFIkEqnY+7HUiMfWUmcLaS2luM4i1pn8prkSSkIIknykxMTEfZegBSky6SU2K/X68zNzHD08GF0XWfTli1cfOmlrFi5kmQqRaFYJF8okC8WCcVm+9HbbuPaV7yC9Zs2sW7Dhris7+ruJp3J8IPvfW/RdUmToefiNxN7HnmE3Q8+uCinUqkU/QMD/PLnP8eyLLZs28bI6tX82513ksnlqNdqdHR2xgPcbC5HNp+np6eHwPfp6u5m45YtNJtNKqUSuTAkEJuIJ0QIs7kc6VyOdDLJyRMnUDWNQi5HIRmysReW50P+4UNfxDAMOvs6UTQFVVNIbdjIwU0XMdp3gnffupMt6ztQlRmSQ32x+GTo+5jZLK5pYrseuu+jA061im8Y3AH8Mgx5IAj4RRCwx3XZlkqxRlHIJxIkUikMsTFJwiE89ZwKfD8+bCptOdkOjgmCAMuymD55Ek/wzjwhIiu5KWEYYjWbzM3OcvTQITRFYd369ey87DJWjYyQSqUotuWUpqo0Gg0+/Fd/xfWvfjVrN2xg0+bNMUm5o7OTQkcH3//2t5/RFtfS+J1sKGf60u675x6MM7DIDSFXsO+xx+Kfzc/NMTc7SyadZvOWLditVtwK0nWd3t5ezr/oIl7+qldxUDB+pbx9PEAjarFJGYmWZUVoJkG8830/Rm15nkc+n496lUrkTjgwNBRtFuJU1o5ll/Io7XLe7SKLYRhSLpVO+wzCIKDZbMYmYS3LwrIskgIquOixZ5izyM92EVdFbBhwClVmJhL0Dw7GsGMJOJDvtdjRQUdnJ51dXRHxSpbwYhOcnpqiVqtx4vhxJkZHyeVytGw7hkRmczlWDA9z+NAh9j76KF3d3ae9147OTsZHR2PnO3hOuv43HQ/u2rWIMCtDP0NOzc7OMjc3RzKZZPPWrbiui2PbUeWs63T39rLt/PN51etfz5FDh5gYHY3aOYaB0raAyftX13Vsy6JcqWCVZ3nr+hK3vsjj7RfavP/5Lq/MPMI3/+wTHLpvP5qmYzk+v/zXfydz+ENcvN4j0f8CPM+lmA05Z2sHQxdsjwAsqhoN1dvUKoqaRjoM+d7CAhZRH18PQ3QiiZXdzSa9qkpG0+iybQLLIiOG5LRd+9naWkuH7zKn2r2MzESCgTZDMkkNcB0H13UpFIsUOzvp7uk5paqhnLL7np6ZoVqtMj46yvHHHycnrMdlTqVSKVauWsXBffvYt2cPnUsk6iFCdM3OzjLf5uDYjlB7JuJZA6VpN8daFEqkraMJNulX/v7v+cVdd1Gv10kmkwwODbFt+3YOHzoUkxk3btlCEAQ8cN991Ot1NEE+Umhb5MWfXdcl8CNfdc/zaNRquG0DdTk7qdVqDAwN0dnVhd1qMTkxEWteBf5iaXop0iihyLJttlRIEqJklacbiZc3DCOCBYs2mSOqBUOIxZ36aE4tFlKfDKJyfqmznLw+OWOan51dVKYH4vV9z6PVajE3N0er2UTVtFhNWD5XoVikUa+Tz+VIZ7MEQUA6lWLLtm2x+dbCwgLHjh4967xHXlP7YucIcuhz8ZsJ6R54pgg5BR3/h698hZ/eeSeVUomkqGC2nXceRw8fjucoazdsIJlMct/dd9Oo11E1LWqfhSHLxOIoW8Ke56H6PhQKhJ7HBzfVOb/DoWQp6ICiqQzmQl7uP8Lff2yeBV9lr+vyJ69YIBx2KTUNut0GiqrjeCoJ0+el//Mm/s9Nt1CbmgLfx2u1UMOQYiLB81otHiWyBs4RQYRdcV9rioKrKBzUdUzfx9d18DwCx4nQV4ZBsDSn1MiDCMAU86Mz5ZScuSSTSQLfZ352NkaKhuI/+Rm3bJuFhYXFORW9IEF7TomDaxiGEVdl27ZYeaNaqXBMWEU80SbRLhQp16NnKp41La9LL7/8NPw3RPIDqqKwaetWPnrrrfzj179OT18fKWGuNTkxwdHDh7ns8ss5Z2SE//a2t6GqKj+9805s2yadTscblfwiJIHQFVbAvrh5GvV6jLxQFCVqNwnmfKyvRaQ8LBf29pmN7J0GguUvLXxz4iZxXDd+nAy52WiaFpOfgiAgk8nEBKwwDHHE3Mlor7LCxcrDnuvGSLL212iHPEvzLF+QHOX7TAl5bJkAuqbFJMx6rUYymYzKdMuKoc8y2dKZTOQfr0ReKQC2ZdHT28sFO3YwPzt72ve9MD/PqpGRRWSs5yqU32zsuOSSRfB1GfKe23ruuXz6E5/ga1/6El1dXZFckGkyNTXFwf372XnZZYysWcMf3Xgj+XyeO26/HcuyyGazMcCkbBhUiQQZdUUhcF2cej1a0BWFF6p1Ls43sZp+JItimmiKSsOGrBGy5RyDk5oGVpWNK21KVQhaNpXHf4ymG2iaiqolGFhW4LovfIErP/ABNvzBH7CQTVPoyvAC1wFVZUbT8Ika+6bvYyoKgabhahpeEGAFASlx3WEY4oYhLdeFIIjY9GJzaM8pORx/spxCgBGk5YVCBAdOpdPoAk3puW5s1gcRtUESQK1GA1VRsB2HhMizVBvnp11RoqevjwsuuuiMHY5KuczAwMAiq4hnOqeeNRvK1nPP5eJLL2VifJyG0LEql8vMzc2x49JL+cDNN/OVL36RsRMnqNdqLB8ejgdgc7OzHD50iDXr17N3zx7u+eUv0TSN+bk5gFNquskkmWw28jYRhlx9AwNkBAu+HZobhpGsSiabJZPNYpgmY6OjOLZNR2fnIsc2OchG3IBSpkTKWdfr9cge1I/MrtrnBHEZ3TYQlNa7cnH2Rf9Y6gfJhd4UJj+pdHpRPxdOLSJyU5OfFW2vIzHzQXhKKRWiE45UAZDkR/l4XdcjQqkYGBYKhcgkSFGwBMLNse2YlwKR4N7E2FgsKzMzNYXjOPzBy1++6Jqf21D+43GmNvK69eu54qqrmJyYoF6v4/s+lUqF6akpLtixg49/5CN89rbbGD1xgmqlEtkyi+cql0oc2LePNevWceTQIX7x85+jKkpsDS318YxkkolsllKxiGMYhIkEbl8fiXyeXKvFUAECFBRVwwzDyIY3myXIZHBNE92ZoGpZFLu7CcOAdBBQIKR0+C4Wjv0UM9tLqKYIFYN8RwfrrrqSwtpR3v/xCv+1p07vYIDal6CgacRZJTY7u60y1zwPzXHIZjJYRB4pzSDA8n0cz8PUNHTRRdANI3aNbY9FOSVmIfIxQVv1ImWWXMfBEeresmpQRdchVs4Q/K5arYZpGGSzWbq7u2O0aFO0q6V0i2EY2LZNNpeLnFMFMGB2ZoZ6rcbLX/nKRdf8TPsLPWtaXqqq8t5bbuFffvAD/vm732Vhfp5zRka4cOdOvv7lL4MoH7NicFiv19m0eTO+7zM/NxdBi/fvZ3jlSibGxwnE4Lu7p4exEydiSYNGvU4YhnT19DC0bBmPPfpohMIQZDrp+QFRdZQS6BbLsuIv0yqXT/lFE91oZiJBX18fc7OzaJpGV3c35VIpQpPoOq64uWVFIz0Q5O/rhhFzV1QBQXTFqb+9CokrGl1fpKKcTCQiwtkSpE87jwaik5Ou6zQajag1oUY6RZrgxkgI5EU7d3JyYoJ9jz0WgxFM04yTpVwq8dM776Szs5NarYbVbFKpVLBaLY4//ji9fX3c/e//HrX8PI8VK1dSLZdxXJct27Zx1YtfzMASYusz6Sz3+xCKovCum29m4+bNfPdb32JudpblK1Zw/Wtew1e/9KVI4l3kVKPRoN5osHHTJgAWFhY4d/t2JsbHGRgcZG5mhpK4N7p6ehg/cSLmbzUbDeaDgEZXF8uGhznx6KMkGw08TaMVaEAQVwCa66GGIYGuEXgWC00d23FYrdZYNgMZ3YuIiwTM3HEz85e+geLwVVTcTh568H6KzqfZMrKHyqgJJQcyIYWszQ4zyZdPOLQUBQNQggBV5JQCpHWdmuNwVMByY80uItWJQFHQRS54ngeKQiKZxBbE6PZoz6mQaM6rC+MrCRAKggBV5JRsfW2/8EIq5TKPPPxwXNGk2roS9UaDf/3xj+nq6qJSLlOxLKrlMq7jcOL4cTo6Orj/nntiZOqK4eHIFthx2LBxI1ddfTXLV6xYdK3PtL/Q78xg60xhGAbXXX89111/ffyz/3nzzRiiLynbQpqYRZycnGRkzRoajQbNRoMwDDl25AjHjx6NZidCpyqXz1Ps6KC0sIDrOPQPDpJKpyNpaHGakafxmDyoRBIlzUYj1s/RNA3btmmI14pnLEGkONy0rNgGtzQ/jy36tKl0Gqdcjk40ROz1pWZfvljcl5pwyZ7uaXDGMFIqlfbAtpCQTwgJh/bWYftz1Ws1Oru7yeVyzM/NRUlOdMIKJDzZ9/n5v/1bBAUuFqlVKnFFJmGjrutSKZcpl0p4nkdXdzcja9ZwcnKS1WvWsHb9+vh7dh2HifFx/uTmmxcZ/ywNCVF+Ln5zoWkaL732Wl567bXxzz52663xzK09p2zbZmJ8nHUbNtBsNqNqOwgYGx3lyOHDVCsVNKE0nM7l6OzooFwqYdt2pC2VzTIzNUU+FBwqVWXPSRfHh7SpYfvRSd1uNtFTSTRd5xcnNS4Ydnjfjhq1WkCqS0EzVDw3oM9pYe/7J6ZP/IB//KTLmnyDzW9vMTelY6QzENqEPniqQj7V4s1pky9YLk4Y4gOegAZngoA5z6MVhriApigYWiQj3x6homAaRuzV47Ra8eyz/aAJp7DfChG4p6OzM5JakXLzohPg+37cyr773/+ddDpNoVCIWPDisCVnpJ7IqWqlgu95dHR2MrJuHXMzMwwPD7Nh8+Y4pzzPY2JsjD9+17vY+ARS8890hfKsaXmdKZrNJocOHiSdydAlzLLkacFMJFgQ5TdhyJp16xgfG+OBXbsigTqBAgmCgFKpRKFQYOPmzbz6da+jZVkcPnCAarUaSc6LltAihFRbi8mxbVKpVCyKJ8vbRDIZz2UURcEW2HHZPpNETHlK0DQtHpxL4TggxqQDi3wWoM2bRTklqx+GYUR0bBuUh2EYu8othd7G70s8tjQ/T6NexxSGQ7K1l06n49fRRJVUXlggnckwvGoVxWKRtDDVAuKNrlQqRdItvs/hgweZnp5ehN6Syqm/eBLBx+fcGn/7EQQBDz3wALlcjlw+H1edEOVUWSAcA99n89atnBwfZ9e995JMJuOefxiGlBcWSGUybNi8mVe+7nU4rsvB/fupVCokxL0cBAFOoPC5+1U0NSSfCDEVn5zuk8bhYTfF//ivNrdeU6G/4NOZh0ozwf/H3ntHWXZVd/6fm18OlXPo6hzVUnerJRBCMhiMDTbYmBlg8NjwW7ZnjQPgcQIPBi8PmhmEwLKwGYIZewwLM4QxUaKVhXJL6la3OlboSu9V1cv5vZt+f9xzb1e1WmJsy5LWoL2WlrpLr6qe3j37nLP3/gbL1lAUF0mVMNZanPyiQWihTfdeBxuZkO2QdlvIu8NILQVZ1XCB3QmVPwqF+EU8ccjBcJg4gKKgyjL+8eEIoIwieeoWfsXiz0IR+eQi2lewwfHSVyxev1/41uS6yHNJzBPXi2rKsmdWVywUCAnUVjKVIhqLBYZ0fsegWCxSLpWQgDOnTpHL5SiVSsF7UFWVeDzO3UeOPO/zfrFz6mV9oKznb/QIC9J2u+0NzoV15komw6Frr+WuI0c4e+oUtVqNgtgE/QGXI7gik1NTLC8vY4vS3dfQkWXPIVE3jKCaSSSTqKpKIpmkr6/Pk6wXsxV/I/ftUcPCZa3dbrO2uko8Hr+ozSUOFH9R+cADvwoJh8O4jhO0q1x4TvHE9dFpt1FV1WMxi5IZCHqqcHFw2Nff7w3z11U5lpBYMQyDaDQaJE5IqB77KDjLtr35j+OwlssFrbpEIhEI0g0MDnqfoaYRjcXQNY3jx45tuAFGo1Gy66RYLhevzFBenPBvzj29vey94gpMYa5mCdh4NpNh/4ED3HfPPTxz8qSXU/l8MIf0Vb/XVlaY2rz54nMV621FOIEa4iIxU4/x3x5L88haikxd5eFKgu84A+y6poWkymgdMF0JVYF0pE2pprG4FmFm1aC01iE3n8WOxQCVdsth+YJDbsFEvcFBiiu4ZQu37SK3bfodh4PhMIddl+tbLVTbJua6RCKRYMbia3j5NsF+WKKjEBIKD/7ltdNub3BxVWSZHkEU3SDYuk62KBaLebIqtk04EgleJ6/LKdu2KeTzwZwlHo+TSqdxXZeBwcEAFORf4k4cO7ZB4DESjbKSzT7vs/6JnaE8VwwNDzN9/jySJDE6NkYsFmNmeppsJsO2HTv4xV/+Zb7+1a9uuBkAVMplUl1dHo9Ckmg1m9x7550e+TEWoyBu6bKiIOEthng8HjDKw+EwI6OjzExPs1wuexusfFEdGAjIXJqQXfE1eRoCneEbdEmuS8uyGB4epioG9LFYDFXTqJTLHvRYJLlj2zRbLU9J+DKKw0H7SpapCOfES8NfvLqu47ouqysryGJGZDsXhSclgSwJRyI0Gw0s2/N/MAyDaCwWVGaObQciln5p3tffz8DgIHUhb28JNzoJr+JqtVoU8/lAyK5Wq7F527bnfdadTudFXfw/iWFZFuObNnHi2DFkSWJQtH9npqfJLi+zafNm3vXv/h1f/4d/QNP1AKAiSRJVoWprGIZnDd3pcNcPf+gRGSMRFNelU6uBqrIoy4xZlifF3umwVnH47pkwZs8IT89M8xe/tUi55NIyZWxFQbctLCH6GA+bNEwN2+xgSC5lR6PZaHDsdofH/9HGtb0jwQhbHP7VYZJLdezHK+TrUc6ndaxyGV3XWRAXOtlxkBsNIqpKw7ICcUhZEsZf/mYve54kl9O59nNe03VwXXK5nKcDJmaZfgdAwtvEw5EI7VYrkGjSDYNYLIZpmheli4Rdt3/R7O7pYWR0lHqtRigcvngR9S+jjsPa2logqlurVtn0PC1kePHnki/LCmVpcZGHH3yQb33jG/x//+E/oCoKy8vLmJ0OkWiUgcFBrr3uOg4dPszn//qvmTl/npnz5z3JD3ETd8HD1fvaXpJEOBLBdV3mL1yg1Wp5+HAh1jY2OUk0FsMFuru7GRgaolQsehWE6wZQ26AlpCjBTMWfvfgEx2ajEYjA+fL7ruuSXVlBU1VPiNK2KeRy3uA/EgnaVH7J6//Zj4C3IX6mKQ6+Z/nHr+OmOK4bVFU+jl4VcyX/dX4FFeiKWZanIrCOMyNJEvFkkq6uLnRdp3dggHw+H7T2bMsCWaanp8cbvouDzv/+aqXC/IULXJib4wuf/SzHnnzysvL8r1Qo//z4cYKa2UyGRx58kG9+/eu859d+jVA4zJKQGPIVKQ4dPsz1N9zAFz/3Oc6dORNc5HRdD4bY5VLJg5CLdReORNAUheLcHFqrhWFZ6M0mRUmiNj6Okkh4UN50GnN0lOVikcH+COm4Q9OUwHZYdRG5ouBKCqrkbcSpkMRaTsLRIBxtsnahgWPJhOMyelii3XS593NZpMMKJ7fHWes4RPN54p0OkVDII0q7rmeQ5bqEXZfwunawL1/kt3ibnQ7mj8kp13ECdXL/Euh7Cq33JzJN0/vMRDfC7HQIiSG//7zi8bhHHtZ1+gYGKJVKgYK431bv7ulh0+bNgc5fS6C+6vU6szMzrGazfO6v/oqnjh69rGDsT3SFUq/X+YtPfIJHHnooULodHh3l5972NlzL4uEHH0SRZV73xjdy8vjxwJo0mUp5G20+jy2Y3o7YKC3TBEliYnKSzPIyK9nsRWgw3izDNE1qlQq9fX1s2rqV82fOcOaZZ1AEtDjQ0ZIukix98hJchPWqmubBM8VmqapqoJ8kiSG/KWC4bVHmSrJn96sbBrIYrPszHL+N5tg2qqZd9DGx7Q3Vy/qhe/B3sRGYlywyn+hkrxsw+t4NM+fPBzpl/vuVFQVTtEf27NvH3UeO4FgWtmWhiZI/t7aGEQrxnW99i5iwZM6trXkJ0moxffYsk1NTGLpONpPh77/0JXbv28c73/OeDaSrVw6UFz5arRa33XILD9x3X+APNDg4yBt/7udQZZkf3X8/ADe+/vWcO3OG2ZmZIKdkcWmyTDOAyncE89t1XSanpljNZilOTwdrxXE9gzYsi7VymdbQEF1TUxw/dw75xAlQFHrSIRRFBryWU85x6dJUUo4FjovrQNJwaJgOjV0673pTnf/5YZuuIRfHUjEbBpLaQI/K1GsSd33cJFrWqTptNB/u32iw2TB4VFFwBFJSdV2ioRBKu01boC9lScIROeX8uJzCq/otcbH0w7mEPOjiyUB1dXUxfe7chs4JePtCp92mq6uLiU2buPvIEa8V7qM9bZvV1VV0Xef73/62R12IRllbXaWaSnHu7FlPGHJ0lFA4zNrqKl/5u79jy7ZtvOe9791wgPxE8FCe6zZ1680388iDD5JMpeju6WFyaopGo8E3/+EfeMe73sWXvvIVvvD3f8/effsol8sMDg0F7SLfJ95eJ4vQEZ7MXV1d5NfWWFpYoNVq0W61vNaOGNyDd5iNjY/TabXQdR1bkAj92YmiqkQiEeJigNbX1xfMMNLpdMBK9qsUn+NhmqZn+iWG91Vx0/DZsI4QjGw2GsQTCWCdV0mng65pwSDfFrMWTdOem/3qf7auS6fdDsyA/ENmPSnSFcmvahrbtm+/WN25blDdNJtNZEXh/rvv5sgPfkBPb28gzd1sNCiXSnQ6Hc8TIxSiUauxtrLCxKZNvO83foOhoSGufc1rOHD11Z5YZDrN+OQkJ44f58SxYxve+iuw4Rc+/sdtt3HfPfeQSCZJplJsmprCsiy+8dWv8ua3vpW/+fKX+Zsvf5mrr7mGfC7H8MgImlC+lSWJ3r4+HNf17AsEyq/RaJASCK+1+XlazSatdtu7xAipHtd1sZtNJsfHMdttYoJNrmoahVKbY9MK1qPBbgAAIABJREFUPUmFcCRMPJ5kTtbJ9Q5ghjUu1EIcmU3T3KES2+KwdkHG7oAsS+hhC0nqUFszmF/SKBdhdbpKFYlQLOaJtjqeDbDcaHAoFsOWJA/55Xpq4YamEdE0dFGht9ttdFW9bE5dqubQFm1ZIxTyqnq/fQyBMoYqkHNBTnHRJVJRVe+yrCg89MAD3P7d79IlPJoc16UlHGbbrRaxWMzzT2k2WVtZYWhkhPf95m8yPjHBwcOHueZVr/LmLqkU45OTnDt7licee2zD+/+JHcovLy3xyIMP0tPb6y1YMfBNpVK0Wy3uvvPO4LVnTp0KPqSJTZuCm5cmTLWGR0dRFIWR0VGmNm+mWql4/u7rylKf1RqJRj3vc0nisUcfJZ/PoxsGqrAV7gimvipmEKZAUrmuy+DQELKiUBDtH0t4g6xHjfnQRT/JfE5HSxj0+C0j//30ig1b1TRi8TiykFzx0SdAoHJ6WSHFSzgr/vBVErc013UDuKg/YFxaWODuI0dQxVDdb9vpol/s94o1XWduZoZSscimzZtRFSXw0pZkmXqjgWmaxJNJZElictMmTMtiaHh4w1uUJIl0Os0jDz204esvdnn+/3rkcznuPnKEXpFTvpNjPJHAMk3u+P73g9eeP3s22FAnJidpiXamqiik02lGRkdRNY2BwUE2b91KvV6nXqsRFu6pkiR561gQcrt6epAVhccfeoh8LoesqsQ0jXijQbTT4fPflWhbKgM9GorcIWw4KHGHbGiAY1/RmCoXGE+0iOYtpLiO44LruGBLKIZNS5dwZRlVtKwcy6JZr3OXLPMl1+VrlsXnLYsLpslP9fUxKsugaUSjUVAUJJFTPru+0+l47azLICTXw4UdQTru7enxZjDrL2aCRCwrCtlMhiN33IEqWty4nsqEr6Pms+t1w2B+bo61XI6pzZsDEnZXV1dw+JimSTQeR1NVpjZvptloPItvAl577OEf/WjD135iW15Li4sgSZTKZbq6ujbcCgzD4MwzzwR/bwm8vNnpMDwywvLSEsVCwSu5HYdQOMy+/ftJpdPcdccdVGu1QLJEXqdv5DNW/dlEJBwmHosxOzNDXRgJASBJ9A0MeNL5wqFwaGSExYUFIpEIJd9PAa/FoMiegJ2P9LJM86J3vdAP8wUb1y/Yplg8Prqj0+l40uGhENFolHKpFFQg61n9G/xWpItij75niWPbAe/Fl4Ho6ukht7YWVCKKYMCDB4TQdJ3lxUUSyaQ3HJRlr7prtz3kWG8vHdP03BtjMUZGR4kIdeR4IsHS4iLPCL+Ny4UmiGDr4xXY8AsbmeVlJEmiXKmQSqU2zORC4TCnT50K/t4xTTJLS9i2zcDAAEtiffgae4ZhsHvvXnp6e7nrhz+kXCqh6TqamMv5LdRAHghv/YcjEVKJBP2zswxUq8jiUtdagU//fS9v+pk0o7E5HC3KuWNjpL55AYUQ0Z0tLNdrMffrbbrGVHILNnpURnZtXMlEwtvAI0KD7nuNBkuui+G3a4HZVovS2ho36jptWaZlWSw3Gh4jPhqlKQA3QCDJdLmcWm9U12w2WVlZQRMt5SCnurtZW1vz5jLiAuojJIdGRjCEk6l/oMsip1rtNhKQTqfRBeclFosxNDxMLBYLbLoX5+c5fuzYs6omP54rp14Mp0Y/XjYHSjwep1qpMDYx8azSs9Pp0NvXh2ma/MXNN/OD73yHC3NzzExPo8gyu/fuZXxigtPCsvR1b3gDD95/v2colExuUPB1Oh1PrkS0u3xkUVTo5UyfP0+tWvVaae5Fufu1lRWisRhTW7awks1y/swZT8xQ171Wj5BKCGStBSzYPyws00Q3DI8stk7KPhKN0hGQYZ9U6HNofIiubdsBmsof6vnVDtJFAy9ZUTDEbWT9wvL9ZyTZ8z5xbJvc6qqH4HIcTNGa8wU4FUXxFFEVhZZoAe7bvz9w61NkmYWFBcKRCGGBtV/JZrn6mmuCha7IMrFYjHA4TL1e9yyPBeYfoFQs8qrXvGbDc/a1116Jf3pcboOJxeNUKhVGRkefRRjtdDr09PRg2zZ/feut/OM3vsHszAyzMzPIssyu3bsZm5jwvOIdhxte9zoef/xxFhcWSKXTVKtV79KiKEidDoqmERLPuNPpEDIM5FCIjiTRNzPDcKlEU9eRxTrXHIeB6TW+9YMkdWMLM9ks/75+lnarRUPXUdwIslzFdkFVZG78Bfjm513MBiiKCzZIto2laaimyWqjwZJloQFGJBKQhnVZJu843K/rqJJE07a9DoLjYNZqRAwDyycCuxedE9fnlC6cGBuNxobLqC0ukqFQCMu2WVtd9XhtrhvMmmTRkZAkKfh82gLyv3ffPtqdDtlMBlVVWZifp29gACMUIh6Ps5LJMPmqVwWHmSzLHjEylaJSqRCLxTbkVLFQYP+VV254zj+5xEZZpm9gILi5++EPAPfu38+HPvhBvvblL5NIpZjavDkw83ny6FEazSbjExNcdfAgTzz2GLMzM2SWlykL3SlNzCJ8Rzm/UvCH1rF4nEwm47HpBR4/ZBiEDANFlgNikqqqDA0Pe5WI+JrPco8JspjrOAwODxOJRgMceSKZ9NwexUEF3ibgo8z8n2UJ5mw0Gt2gKeYP0yMCqQYE9sP+TEWWPPfEVqsVtMkCUUiRRP62I4nX+rchH1Pva481RWWj67rnXCn85Q3DCAhfsXgcTdzE2q1W8Oz8QyqRTHLda1/L2upqwI+xbZt8Po+maRy69toNz/qVCuWFDdtxGB0be1ZO+dyuA4cO8dEPfYi//cIXiMbjbNm2LdignnrySSrlMmPj4xw4dIiTJ04wffYsK9ksxWIRQ9e9jUpVscQlQxPwV0u0j6LpNI1Mht61NWqm6d3KDQMtFMISrx90XVqqysjEBEONBnWRB+fv86riWCJGJJGgp8vlLR8bZu+bY8T7ZEwtgpFMErUsLE1jFQL/In9G2lIU6qIqKQlX1CCn8A4FQ5aJi3kmXJyDaP5eIQ6AZqMRiDv6Ol7+v+EiybEtKo5YPB4IxWq6Tq1aDboFuvjsNMPwrIEFEMYWQBxDtKQty6IuLqo+ojMpcqqYz9MSvky2bVMsFnFdl1ddf/2GZ+13PF6sUP70T//0+f778/7Hf0ms18dZWVkhn8/zpp/7Oe656y6P7NPpUC6VqNfrDI+O8sA993DXkSOYlkUhl8OxbSampjzklGnS29vL5KZNQUumWi57ir+KQiGXIy3IipqqMjYxges46LpOf38/tmVRrVQwDIPG+tmGaJ/5/gW5tTV0XfdkVhqNQAxSkmXi8bg3tDTNwDfel5zvCLkWHw3iI8t8uXrX9TxUfLFJv+w1DCM43HxTMB9y7H+PqqqerpeY1awv1YGLNsfiZhiJRi9WLOLzTyaTAIGicTwe59rrriObzQbggXA4jClQc+1Oh67ubq/X3mp5Ei6uy8jYGO12m8cefhjLNFlcXOTsqVMeF0GoDFTKZbp7enjHu99Nf3//hjbMAw88QF9fH3v37n2hl9tHX+gf+C+IP/3X+sHrcyqfz7O0tMSbf/7neeCee1hbW6PT6VApl6nVaoyMjvLQAw9w5Pbb6ZgmxXwe0zSZnJoK5iGxeJzde/dSq1YxDINCLuflkKKQy+VIipxCVRkcHw8223R/P5bkCUwORiIM1GrYfk7ZNlo4jBYKISkK5bU1ZnSddqvFwWYT13VpmyatskRXf5yBnQ4OnsWEMagztkNGO2mw+VwTu14nLGC7a7rOhU4HVXBLOq6LKcA1rusS13VU00TTNHKmSdWyaDoOTdMkGoshi3xXxeVM9XNq3ed6aU758OKIqIgCkqPrBnNEfzYTiUS47vrrWRVVTADHdhzMTodWu01XdzdjExM4psna6iqO6zIyMoJl2zz+yCM0mk2ymQzPnDzp8cQ6HSoip1KpFL/8rncxNDy8IaceffRRQqEQBw8efKGX22Vz6iVvedXrdaanpzlw4AC6rvOZz3+e++65h9PPPEN3Tw/jExPcfNNNjI6Po+t6IOVcr9fJra6ybfv24FZQLBTo6esju7xMpVLBtCzC4TCJZJJKuUx4XSk8PjlJPB7nyaNHyedyAVzWH8o5jhPIMuiCuGjoOolEglX/xi1UVx3hM79561ZmZ2Y8u8/eXvJra145LH6n38byy2q/avDRXr4SqV8RVatV4OJCdl2XYqGAqmleVSJaUo7rEo1Gg4Hh+vArHP/PZqfDVYcOceyJJ4LqzG9z+Xwb8IiIe/bs4fhTTwUwyXgiweLCAo7jsDQ/z2o2630mqRS1apViPs/C/DypdJoDV18dIPAyy8sMDg3xrl/5FQzDoKu7OwAM+Mq169uPr8S/LJrNJqdPn+bAgQMYhsGtn/scP7rvPp4+dox0Os3Uli184uMfZ2xszKt6RU61hMvgzt27sUyTfKHAysoK3b29lItFavU6tiD9JtNpKqUSEaGyYLouqZER0l1dHH/iCSrZLLKmeWZXioIFAZBERlTVloWt6ySTSZy1NeaaTVK2DYqCZbk8+DdttMYO0ptmqCZlZs72EP1KHu2Cwppu0ux0kAHDstjqONwlSXTE/EQSa0sW88yIJGErCnO1GibrcgooFIsYqkpI05BUlZaopiPR6EWxSBEBH0xQCHBd2p0O+w8c4OljxzzWuyRhtttohuGBWkQHolQqsXffPp48ejRog8XjcS7MzeHYNkuS5F1aNc3THiwWKZVKLC8uEonFuPqaawKycjaToauri/e8972EQiG6uruDamV9TrXb7QCM8WLES3qgWJbF8ePH2b17d7CRJFMp3vwLv8Cbf+EXAPjLT30KTdeD3rrvkhYKh8mLSqbZbHpcFEni3JkznD55kkKhgFwqeeSrcJjRiYlAyO3qa67hkYcfplatMjQ8TLFYDAiFtm2jCGSGDz2O+azxaJRMJkNudTXghPgDvWajQTaToa+vj2KhwNrKimf1q6qEIpGgPNWEHlnE/5rY8AN2u2EEC9o38PJnL5I44Cxxq/LLYFlRaIo5USgcptVqbfCl929YvlujIssMDA5yYXY2OGx8cy1Zlsnlcvzo3nuJxeOMjo+zuLjoHbqirWZEIoHneK1Wo7i0xMc+/nGmtm7lM5/+NBOTk8EzlmWZ4ZGRwHZgeGQk+Lr/3vyKqypuwq/EPz9s2+bYsWPs3Lkz+Czj8Thv/Nmf5Y0/+7MAfOlzn0MRaCJ/bicrCqFQyHNYbLWwOh2Swnr2wswMz5w4QTGfpyRJAYBlZGwMTdMol8scPHyYp554gurcHCPDw5zO5VBkmarjUHAc0rKMHIsB3gUmFo/jmibZaBQtm+XgygomYMsyIcACOs0Wz3xngWi6j/9RKDDprvGz5TIV/703GuA4uKpK0ra5JhLh4VbLu+hB4HMUMQwyor1dtyw0RSEkugCI/5+WZXkeROJy58PiZVkmHA5vmEkGOSUALaFQCAkYGR3l/NmzXk6Jg82yLCRZppDP89D993tupmNjLC4uUioWAwCOrmmku7pQFIVatcrCwgJ/+Cd/wsFDh7jlE59gfGIimJVIkqdwMH/hAqVCgT1XXBG8r0tzqiYASS9WvGQzFNd1OXHiBOPj4yQE/+JykV1eJhwOo6kqw6OjgemMf8NvNZvUajWuve46Thw/zqMPPogL9Pb2BrftSqVCsVCgf3CQnbt3M3/hAoV8nlqtRmZ5ORjKh8Jhz2RI8DF8N0cfd18sFMhmMkEbSlXVoC8bCoeplEo4jsPW7dsD1qokSdSrVQ8OKH4+QDQW86CVgqXrKxr7hEL/QLAEFNOH+OK6wWETj8cvSjOIfm5KbAJ++Hwa33K0VCrxyIMPUq9WiScS3u1RtNR0AXmWZRldkCqfPnaMeCzGPqH5pIgZkmma1BsNurq72bV7N08ePUqlXH6Wh4QfsqIwNzu78Wuyp/+li9bdkSNHKF7GOOiV+L+PU6dOMTQ09Ly30qWlJQ8koSiMjY/TEK0mH/7bbrUol8tcf+ONnBD+Qo7j0NPXF4gklstl8rkcvX197N67l+zSEqsrKzQaDRYWF9FEbujhME9FIsi6ThiQTBPdcaDRoJNKES+XmVxawuh0kCyLoqJQi0axZBklFGKuWuVrnQ5927fTY5pYrosry5jVKjFNQwuFCIucOhCJ8KbeXoZlGUlRiIdChIV1hAKYPhHZcTyzOzFL9QVY3XU55SO7XMchmUqhquqG2YuqaR4dQFEol0o89uijFAsFEolEIGJr257/kf/zdNHCPnbsGIZhsG///oDvMzQygi1mJul0mr379vHU0aOUK5WgU3Jp6LrO+XPnNnxtfU7pus4Pf/hDcsIX6sWIl6xCmZubQ9f1QJfmuWJsYoITTz+NC+zas4dqpRLAZ/2Fv3nbNn7w3e+yvLTktaJKJRRFoaunxxsWC1/2waEhFEXxKgdN82CuhkGxUCAciRBPJNB0nW5RPmYzGQ/JFA6zurJCVGh9+Z1qn/QYCofRhC5XrVbDWVqip7eX1WwWR/A+DEEWa7VangaY6M9GYzFawiXSl6CPJxKBeJyPMtN1PSBH+TyaWrXKyNgYy4uLgWicL4xZrVa9GYmYyTiC7+JXVgUBPkilUqS7uigUCt6gXegNyZKEJWYnlm0zPDbGQz/6Eal0mkq5zP4DB4gLt0XXdTl7+jRvestbePay98KxbaLihnppyLLM1772NQ4dOsR73vOef9pCeiUA7+KyINqRo6Ojz/va8clJHn34YVzXZfvOnZTLZQrCh7zTblMuldiybRs//P73WV5c9HKqXEaRZbq6u4OuQDgSYXRsDMu2PQdPcTELh8MsC+vgRCJB2zA4lkyyTVFgaQk7FGI1HsfKZJAjERLttudJAsidDlVFwQ6F0HQdqVKhXa+zurjI1p4edMtiQcw6hnWdFQgORNu2GXJdYrEYT7ZatG2b8+02iiRhCM8XhOKF47rIQoLIzynbsqgLpOnC/HxwKczn854Fb6USrG+fo+XnnZ9TluBh+X5IlmlezClF8cQiw2Fsx2F4dJTHHn44yKkrrrwyEFx1XdfzdbpUBmZdWJb1vDn1ve99j7GxMX73d3/3n7aY/gXxklQozWaTtbU1tm/f/mNf+/o3vMHzOW820XWdV19/PQcPHyadTnPVoUPs2rOH++68k2wmgyagi74+VW51lVAoxBVXXkk4EqGQz5NKp9F1nVaz6Xk5NJt09/Z6cMV6nbbQ+IrGYuy/6ip0TWNRsIHr1epFUqAse4x628YwjKC3mUwmCUejFAsFGo1GgH4ql0oMDQ/TIwhf4VCIsMCHxxOJDQrBtWo1cGUMYMdituIvrlg8jqbr5NfWNvR4O52Ox8aHgKHvS+77mmGSIFVJsuwN4gXMUVEU6rUaE5OT7N2/35t39PTQbDQ8y1HJE46UZZnM4mLwO31I9PadO9GEMOT68BWdffOmSyOXy/HJT36Sm2+++TmT55V4/uh0OiwvL7Nr164f+xnecOONgNemVTWNa179aq6+9lq6urvZc8UVXHHVVdx/zz0sLS56OSW0uzpiWKzrOnv27SOZSpFZXqaruxtN12k2GsG6j/f20mm3aYqcqts2M5EIy1ddxUPRKGsXLlBttajWajjCa0iWJE+QUogpGqEQuC5aPE40FuNIsch/r9f5cqvF/2w0+JtymeTAAFP9/aiyTCMcZjka5aT4nrrQ9JMliXathiats6cQB9ClOWUYBrlLcsoUYIb1OeU7YEZEh0KSpKDDYAlnUp+/VavVGB4ZYf+VVwYzRMs0KRYKgUSSqqosLiwEv9Of/2zbsQNjHS3BD0tItewV7a5Lo1Kp8NGPfpRbb731Rc2plwTlpWnasxA+fjQaDe45coS//Zu/4d477ySZStHb389Z4bVQrVRwHcdDowCzMzM0Gw0kWaZYLKLIsodEEQPh4bExz1WuVqN/YADTNLn3rrvI5/NUq1Vq1SrtVouJyUl6+/qwHYfde/eyls2yurLC2MQEiwsLwaKxTBMXiAq/6HarRSKVCgx4DMOgUChQLpW8MhkCKZNKueyx38WizuXzgW6WpusbhtK+OViAh5flYH7ktyWajYbHhVnnybK+z6quE7GDiySnUDjsaYmJIfzo+Dh5QWLzZzOpdJqcmJt0TJPegYFAT8hXF/YZ8HPT04xPTtLpdBgaHuaZkycDzH5RfBa/9G/+Ddt37nzW83Zdl/e///28973v5dChQy/8YvPi/3mUl6Ioz5lTzWaT+++5h7/94he5+8gRQqEQo+PjnD55MkAJOY7DxOQkqqYxPzdHVRjUlQoFAJLpNLqmYTsOQ8PDJFMpT/+uvx+Ae++8k3wuR7VSoV6r0Wg2GRgbY1C0R3fu3ctKPs/i0hLjExOE5+cxBay9bVn0Og4h4WlvtloYySSYJh3bZjUcplYqsVQo4CoKSUBzXequy/FKhcPRKI8rCiccx8sp0UayQiFK63gYiuyx5X2rBj+nfCuLVrtNrdEIeCKX5hQQ5Jov3tjpdNCFBYSfU0gSE5s2eQoBgk7g2HZAfNQ0DbPToaevj2QyycrKiue31G4zIqrL+bk5BgYHsWyb0dFRTj3zDLVaDfAEOouFAm9+61vZt3//s56367p86EMf4i1veQs33HDDC7bGLomXF8rrcqdmpVzmP//hH7K4sOAN7VyXE8ePk0il+NP/8l9YyWZpt1r0Dw3xV5/+NEMjI6yurnpeJrpOT28vmaUlzyxKcEB0XWdpYYHtu3Zx5tQpZmdmUBWFru5uzxnNtmkLi80rDxxg69atuI5DqVQiFA574m5iUcBFkqBlWYGYXFNAjQ3DYGlhYYPjYiwep9loBNDeQj7Pzt27vduIWHztdtvzYRCGXc1GI1A9LhYKgbikL2gpidtVoPklZiz+Pz6WXjeMAP4sSVLgyOiX6/4AL5VKsXf/fk6fPOkxq9fJ4jcEU79Zr9Pb10d+bY1cLkdPTw/lUilw8ksmk9zxve9hi8TZsXMnuVyObTt2cO11120Y1K+Pu+66i1KpxDve8Y4XcHX9ZIY/R1sftVqNj334w0yfPx+YPZ08cYJoNMqHP/Yxb301GvQNDvL5v/orkqmU510uhE17+vpYWlgIKlzwgCNLCwts2rKFmfPnmZ2eRhLtsEql4nGpOh0W5ufp7etjdOtWTEkil89jGAbnz59nh6KQjEZxRPu2btv0CNFRGZDEELwcDhNaWmLWstAcB1NRqCYSqI0GuqJQcV0+kcuxZc8elEwGp1gETcPtdIiIGYamabSaTSTXpaeri2KpRLVe9y5dIqfAs7DwiYs+CivIKUF81DQt4IXJ/r9FTvnSLY7jEI1GufKqq3j6+PHgOTiup/nlV4aNep2u7m5SqRSrKyukUikq5TIz09MURJvtzttvxxKcrj379pFbW2Pz1q1c++pXM/Uc0vWPPPIIp0+f5rbbbnvB19iPi5ccNrw+vvaVr7C4sBCc0uDJEZw+dYp777qLX/v1Xwfg6WPHPOJhKEQ6nWZ5aYmQYJfW4vEALWR2OrSbTcYmJljNZsksL9NoNNA1jVa77akSi+FZMpUiHA7TPzjoebwL7SrLsgLVX3/gpqoq/YODnD97NpBFyOfzGALWXCmXg429Vqt5JDDDoNNu0261WFxYwDAMb0AvqhVZkujr6wtguYqieAZeAksvi8TzB326rlOr14ME2CArI3kaQ416na07drCayVAUc6WOaRJdd/AkUimmz5/nqkOHyGYylIrFAOLYbrWoVCrEEwmeefppkDxW8ODgIFu3b8dxXSLhMFcfPhxsNgCLou/+/t///ed93vV6nQ9/+MN8+9vffs5h/ivxL4tvf/ObzJw/v2GukkqlOHPmDLd/73v89gc/CMDZM2dotVr09PSQSqUCAEVUkHLrtRqWEIdsNZsMj4xQEiCVer3uVevttjcDELOFZCoVqO5qolWsG4bHKg+FGG+3MZJJHE1jGegZG8M8c4a2qpJLJGgVCqBpOLEYZrGIIkngODSrVSzDQDIM6HRotNssivZcJBbDASzHISJJ7Bsc5Pj8PJZATJqdDoaqYgsAimXbtE2TcCiEHol4VYAPC16fU+JrzWaTzVu3UiwWg6reh9/7xOREMsnM9DQHr76a7p4e8rkckgDEmJ0OxWKReCLB6WeeCWgJA0NDbN6yJeCu/dRP/zSxdfMRPzd/XE61221+7/d+jy9/+csvSU69ZFl8aYXiOA533nEH/QMDz3ptuquLO++4I/i7f3sAGBFY+o4YaKe6utiydSt9/f1s37WLsclJnhSSEX6P1MUbEhdyOcKRCHv27fNaVfk8iUSCcDhMxzTJra5imiaJZBJd6OTU6/Xg+zdv2UL/wIDnH99sUhfS9T7Ba73Fr2EY2OKg0DQt4IvUKhUatRrVSoVSucymzZu91+i6t5jBs+aV5QAG6Q9FdU3b+DkKAICPfU+mUjTqdc/YSiBZJPHefRJktVJheWmJe374Q0KhEMlkklg8TlXYmcbi8eBW5hsQabrOf/3Up9i1ezeTU1MbDhPw5NGPPv74cxqA+fHnf/7nvPe972XsMkJ3r8QLE7d/73v09vY+6+vdPT3cd889QUt1PX9paGTE414JT/V0Os2mLVsYGBhgy7ZtTG3ZwvGnnmL+wgVi8XhgDexX4LrQ/QqHw2SXlkil054TqG1TWFvD6XSoplJUDQOaHkFRB8qmSWnzZo4ODXGyWPQQnEKgFYFS1ARqShZK3/5a14SsfBZ4tFLhaL3OQ9Uqy4UCBzZvJq6qyALt6Arou28FIUsSHdP0LptC9iio9PyqBG/PSqXT1Gs1Nm/ZEqDDfKkWP0drtRor2Sz33HkniqJ4fvOJBLVyGVVo3Wnr7CjqtRqu63LTLbdw5YEDTExObjhMAPoHBnjm6adZW1193uf9yU9+kre97W3/V/Ppf4142VQophAavJyEtKbrVEolj0XbbjM3M8PRxx7jxNNPMzIywoGDBzkJfo1oAAAgAElEQVT21FNUKxWazSbd3d1MTk0FiqiGYWAYRsDoTnd1eYP+VisQTpMkCSMUwrZtHnvkEYqFgvd+BKxwYGiIyakpZs+fZ3R8nHKpRDaTYXJqilq1SrPV8hi6YhCniwrHd13zJa5V4cRYKBS8hJUveliXCgXazWbAgbFtO2iXxaLRwNvFr1aisVjQtgOCQaPP8SgWixQF8qSru9vzqBa3rKAX7CusShIz58+jGwY3vv71NOp1HnvkESLRKBOTk4EL5cDgIM1Gg+NPPcXqykoAg14fPnmtWq16TOrLxBNPPMHRo0e5+eabX7A19EpsDFdUqZeD5auiAvaVHS7MzfHU0aOcPHaMwZERrjp4kOPrciqRSrF5y5Ygp3RdJxwOU8jncR1nQ075jo5IEiEhFXTs0Uep5vO0heq367qYvb1cvWsX9TNnSI2OcqJWY3FpidFNmzAaDVyhRuGaJhFZxhQ5ZfqMeDxBx7Cu07YszheL5BoNNB9e77rMFQqUm01GEgkWLQvZdak3GqiKQjgapWNZgdOqKZSS/U6AH5ZlBTPUQqHg8dUch57eXorFosdZaTbRVBVXsN9lMZeZm51FVVVufN3rMC2LRx58MPCTbzWb2I5D/7ZttFstHn/kEVZWVi6rZ+erGFer1cAF9dI4deoUt99+Ow888MALtIL+6fGy6TPous7Epk2XvdVWy2W2bttGqVTiD97/fr742c8SiUQo5HKcOH6cxx97jEOHDzO1dSuvfd3r+O+f/jSSJDE2Ph7cLlRVpbunB2cdxFHTNIxQiFKx6JWkxSJf/+pXeebEicBr3ScdrmazlEslxiYnGRwaCvwKssKjPhwOE4/HSaZSxGIxFFXFFIN6Hy6sCbOtbDbrtRCEXpgv9+ATBaOxGKPj44EQJIL0Nzg8vNFy1HVJpdMXqxTxNb9N5r9udWXFg4WKG5YhdLlkQVDr6e0lkUwSDofRdZ2QsCj1ET4r2Szbd+zw/B1UlVKpxPFjxxgZHaUm2Pzrw7Ks4HddLkzT5AMf+ACf+cxnLi/B/0q8ICFJEjt27vQQepdEvVYLpHI+/Pu/z22f+pSXU4UCp06c4JGHHuLKgwfZun07111/Pf/9L/4CVVUZGR0NKm9FzCL99e1zHwzD8Kx0bZt6vc4//sM/cPKpp9B0nWg8HuRUKZfj/OoqzYkJqsPD1EyTUDRKNpOhJpwkY/E4iVSK8VgMXVVpCVSi4zi0Gw3QNArNJucyGTLVqscPE9p9ikCPrTYa5AyDkYkJGgK277ou9Wp1g6eSPytJX6J27ls+OI4TOFXmVlfJra0BXls+FArREHOf8PqcCoUIGUYwz/R9lbLLy2zZvp3tO3eiGwaVSoUnn3iCkZGRYPi+PvyZaVdX12WftW3bvP/97+fWW299SdUmXjYHiiRJ/Nt3v5tKpbJBzK5er9NoNIhEo7zzbW/jB9/+Ngvz8xihEJs2b8YIhahWqzz68MP83Fvewsdvvvmij7MoL302uKqqdHV1BS2jTrtNWwy8o7EY5UqF1ZUVTNOkUa8jS1JgYDM8MoJpmoyOjtLX3+8ZXYmZiSLw5f5wTlEU9l1xBd09PYHoo6qqdDodkul0QFqUJMkzLnKcAH3lui7VcplKuczo+DihcJiw0BPThZ+DP3RHwH1VoYbqE6pMofaqit8N3k0uJAaUpgATWKI9t3XbtoCTAwRaaj5BytcMUhSFRCIR/JPq7iafzwcCdiDw80tLXPfa1wYKzpfGrbfeyhve8Ab27Nnzwi+kn+C4HNDl7e98J41Gg/q6TarZaFCtVunq6uLdv/RL/J///b9ZmJ9HUVU2bdlCKBymUa/z0AMP8Lo3vpGbbrkFTazfUCi0wQhOURR6+vowRU61Wy1PPduySKVSVMtlctms11ISM794IkEilWJ0fBy702FwaIj+gQEskVPRaJSSqmLaNqog6oUUhZ/at4/NfX1oqoojy0iGQb3TIZROY3lvCFmSqDcadGwbQiHUSATHdSlWq5SLRcYnJgiFQoQjES8fFCWA7ftdBH/t+0hLXzEjFAoFbHTw2vS6kEGyxEDfEVDlbdu3E0skgpzK53Leoeu/3rYpl0oeKjUeJx6P09PbS0woCVfXPS/XdVleWuLQ4cPPeUn74he/yP79+7n66qtfkLX0z42XTcsL4KpDh/jtD36QL33+85SWlwE8X4CREU498wzFQoF4MomiKCwvLtLd08NVBw/SabfJ5/O8+a1vfdYm1tPb6/Uvq1Uigg0/MDyMZZqYpsnA4CDnzpzB7HTo7u0N/LNd0QvtHxhgcmoKs9Nhenqa1GtfC3gSMWurq57Nr6YRTyQ8KK/jIOOhOmLxOJFolNzaWjCor9dqQcL6PBbXdTFCoQCtpRuGt7AFI3+9A+XYxIRnWVytetwRsemDh5fH9WxV/bZWRLTKfCLkzt27iRSLLM7Pe9L5nQ71ej04XBzHodNuMzw6yuI6cpdvOZzP5SgVizzx6KMkUykOXn01D/3oR8iyTDQaRVVVDhw8yFvf/vbLPuNz587xzW9+kx9dYgT0SvzrxO69e/m9P/5jPv+Zz5DJZMB1PULi+DhnT5+mWCgQSyTQVJWVTIZkOs2VBw5gdjqsra3xpje/mXgiQUbkI3gzzS6BQIzGYhiGwSYhKtlsNhkWEiStRoO+/n50obDtOA71ep1INMqmzZsBOHXiBNe+7nUARFMpVhcXvVmHppGJx5nodHBaLe/mW6sxFonQmZpidnWVUqnkGVdVq8iGgbTOltp0XeKhULBuDSFx0qrXsWyberOJ47rUWy3GJyexLItysejllBBw9T2EgA0GWb5+mWVZNBoNtu3cSSKVYm56OsiparUaKFwgSZiWxcjoKAtzc4H9t//eSqI1ffzoURLpNAcOH+ah++9HkiRi0SiyorBn3z7e8e53X/YZLy0t8YUvfIEHH3zwX2EF/dPiJYUNXwpxBLj+xhu59rrruDA3hy8Y+Cd/8AeMjo0xc/58IMkejcUo5PM0Gg2vxSSEEmPxOMVCgZNPP83Jp5+mt6+PXXv2cO7MGXJra7Tbbbp7exkeHqYp5hUuEBdILUvAaJGkwKfEsqyAbQ/eppoVUveSqADCoRBXv+pVnDl1imgshm1ZrGSz9Pb10dff75EVxYZtihuQX1Gsr1h8opPjOGQuXAj0ulzXxTJNZs+fJxQOExLtqVarhWWansdLJOIN/gXaBAgqI39WszA/z+49e8hmMt7vxDuIotEoeaHiXKlWMfJ5du3Zw9PHj9NsNKjX6xQKBeYvXGDr9u20BbKmUipx8Oqr2bt/P5nlZYZHRzl46NBlNbkcx+H9738/t9xyy4tq+vOTHoevvZYDhw5xYW4uQBT+wQc+wNDwMIsLCx40VihRl4tFqtUqScHl8pUdCoUCp595hhPHj9PT28u2HTuYPn+e1ZUV2q0WqXSawaEhmo0GyVQK27JIpFKBAkNCGHx1Oh0skVNmp4Mu1kG5UGBucZGyQES5ksSqrjN53XVUT58mGg5z1nGYWVkh1t1Nz9AQxTNnvJxyXWRRqSuqii0UfoFAyihsGJi2zdLKCqaYgbiui2VZzExPE4lEPIixYdBptQKrCT+nZIEIAy+n/MsfeG6z+/bvZ3F+PvjMO+02kWiU1ZUVr/VXrXqUgT17OHH8OE3ReZmZnubC7CybNm+mY1mesO3p0+zbv5+Dhw+ztLjIwNAQh66+mnA4/Kxn67ouH/jAB7jpppueNch/KeJlVaH4oWkamwXG+gff/a5ntQn0Cljt+iqkVq0ii369pml8/KMf5YF778UQvf9KqcTc7CxXHTxId28vo2NjvO83f5NP3nQTPb291AUeHQj6v/4h5Ze9AOVKha6uLo7cfjuL8/NoQnnYv923Wi2OP/EEI2NjDI2MUKtUyOfz1KpVTMsKDgBfcTgcDlMsFgNila8PpmkaK9lssNhVMcfwDwXXcWjU60xt3UqtWvX0sxQFyzQDhFo+lwtQXL6CsCvmPJZoPUxt2cL5s2eDAaRt29RqNSRZZnl+PmDCJ9NpXv2a17B7717uvftuBoeGGBgcBCCKB0F9+sknec0NN/DTb3wjpVKJ2dlZbNtmYmJig/vm//pf/4utW7dy3XXXvSjr6JW4GKqwjwW49667AgmR3r4+ZqenNwgIVstlDMMItOc+9d/+G3f84AcYuk4hn6dSLjM/N8f+q66iq6uL/oEBfuO3f5vbbrmFru5uT8hUbOSKqqKFwzQbjQAxqIhLYSGfp7u/n7tvv53cwoKnjZdM0hJoyU6nw48ef5yhsTHskRGkdhsll/MUKEolT+NPILcs0yQcibBUKASSRb4OnqbrLGSz3iVRKINHBJFQEpe8aq3Glq1badTrZCqVQCxVi8UYGBhgJZsNfON9JXK/s+CI9tW27ds5c+qUx1UT7eiGQIUuLy+TyWSQJIlEIsGNr389V1x1FQ8+8AA9PT0Mr4N1J1MpTp88yate8xp+5dd+jUqlwtzcHNPT04yPj9PT0xPk1Le+9S0SiQQ/8zM/8+IspB8TL5sZynOFbyfqui6bNm/Gl2Re74SYW1ujq7ub977rXfzPL3yBpcVFXMdh2/btxJNJbMvi6GOP8cvvfCcfu+kmFHFTMkIhTwtHlLMhwyCdTqPIwlRHaAQVCwVSySTNZpP5CxcCw51Gs0k0FiOZSrF52zZcPG+RwcFBr6wVyIxgdiH0fMBL5CkBEfY9F1wh5OiX7UCAnNF0naiQTnFdl5VMhuHhYa+tJhK/0WgwNDyML4vvh//5tdpt8rkcRx97jPzaGv0DA4QjEVZXVsjlciQSCU9UUwwzfcXVN73lLfzsz/88qqI8C9YtSRLxZJL777kH8A6YK664gu3bt5PNZnnssceYnZ1lYWGB2267jZtuuukVeZWXODQhbOi6LhOTk0F1H4gfOg6rKysMDA7ym7/6q3z2tts8y23LYuv27QF/6/FHH+Wtb387f/6JTxCJRKjXakQiESLRqDcfFIZs8a4uZAHtbzYaSECpUCCRStFut8nMzweae7V6HT0WI5xMMrlliyfLEo8zODzsVR6CZxURh4mv5gsw1N3N3q1bMVQVV5KwTdMj8KoqIYHgBALBV78C8X3eM0tL9PX3k0ilgpxqt1r0CQUCV7ST4aKMvdnpkMvleOqJJ8gsLzMwNEQ4EiG3usra6irReJzevj5URUEVSLFmo8GNb3gDb33723Edzx54fUiSRDKd5t477wQgkUiwd+9edgqy8KOPPsrs7CyZTIabbrqJW2655WWTUy/7A2XPvn1IkucumEwmueZVr8IwDOq1Gu12m1A4zPDoKEsLCwEMOByJBG2p3Xv3cs2rX83ExASbNm0KII1+8hiGQW9/fyCpIssyw2NjTG3ZQrqri77+fhzHYXZmJmCV+5u6KUrrrdu20d3dHTglgnfL6Kw7+FQhRtluNrFMk5YoqweHh0kkk8Eh1mo2kQUr3idXAgEKzLcljkQiLC0ueogz4T3fqNdZXlpiaGQk4MLUqtVAEkaWPA8HSZbJZDLMTk+za/dubvzpnw60mjRNY+fu3WzfuZMrr7qKLVu38o/f+EZgXHa5hauq6oahL3iEuF27drF3714eeOABbrzxRg4fPvyiSmm/EpePnbt3IwuoazQW45pXvzo4ENoC9js6Ps7iwoLnA2QYRCIRioUCS4uL7Ni1i8OvfjWbNm9mdHyccDgceM/7boeDIyPB/EGSJLqHhpjYvp14Ok334CAd12V6epp2q0V3V9dFKLGo1Lds305Xby+tZhPV52Mlk95cEU/eXtU0orEYbZFLnXYbudNh0+AgPakUhqrSarVot1o4eNVSkFOSFNhZ+wivcDQaePf4vJR6vc7iwgLDo6MBFNjnjRihUHCY+WjK8+fOsX3HDn7qDW9AljxfFEVRgpzaf9VV7Ni1i+9861tB1+FyOaUJJv36iEQi7Nixg/379/Pkk09y/fXXs2vXrpdV+/hleaA4jsP0+fOcOH4cVVX5pXe8g+XFRc6dOeN5MV9xBdt27OA/fehDfOy//ldq1Sqj4+PeYFncAiLRqNcSEtwLSZZpiWGYJEksLy1x5Ac/4Ef33UdakCGbQgpf0zRSqRTj4+Ns2bbNY8knk7RaLer1OrphkEylSIgF3my1gtuPDw9cWVmhUi6zms2ykslQzOeZnJqif3CQeDyOZdvUKhVqQowxHIkEm32n0yElvBGCz8S2WT9x0gU5ciWbpdNueyrItk0+l2NpcdGrjAyDmAALmJaFruv09vYyPDJCLBbzynVBdux0OqQFaatWq5FIJAL12MzyMgODg2jCaOzSqFQql9UUAjzzn64uDhw4wJYtW/jqV7/6Qi2TV+KSeL5bquu6zM3McOL4cSzL4p3veQ+rKyucPXMGSZLYs28f23bs4Ld+7/e4+dZbqZRKHgpLtI8kSSIciQRCof7XfGi74zjk1taCnIpGImzbsYNWs0m1WvUQkbEYQ+PjTGzZ4qnyJhIBglDTdVLpNEnRUWg0GgH8XBPzuIWVFeYqFaYzGc5mMszk8/SNjjI6Ouqpdts2a7UaK0KF2+dIyZLnrOpfBv3wZyI+3N7QdUzTJCNUy81OB9txKBYKLFy4gKwont97IhFcCDVNo7u7m5HRUWLxeOAvrygKzWaTVFeXJw5bLpNIJDw1DdEV6OruJhqLbfBa8aNcLD5nTum6Tk9PD9u2beOaa67h7/7u7/5Z6+VfI152M5Tp8+f5y1tuYTWb9fqbts31N97In/zZn/GD73yH8+fOke7u5nd+9Vc5fO213H/PPYGXdE9f3wY0iiRJVITvh+M49A8M8H++/nX++i//0pPpLpdZy+VYzWaZnJpix86dDI+O8r7f+A2+9LnPURXfawoBOJ+XUa1UcA3DgxAKRvza6irRWIwzZ87wxOOPs7a25lnfNpuYgtn/1NGjDA4Ps33XLvoHBrj7zjuJhMMsiLlQOBLBEMgvX3m1Jdpu/uzFT+Tl5eVg8L4eLeb3eMOicstms4Hiqm3bDA0Po6hqoBqbWV6mt7/fOwiFTP7aygqDYk5Sr9cZHBrCMAx+/m1v48t/+7cMiL+7rks+l/PsTQX67dKoVCp87GMf44477gh+5ivx4sb83Bx/ecstLC4uBpI7r7ruOv7zn/0Zd3z/+5w+dYpEIsFv/s7v8JrXvpZHH344AL909/QwPzcX/CxZlimXSqTTaRzHYXB4mO9/+9v85ac+FRwe+VyOlWyWsfFxduzaRU9vL7/+H/8jX//qV1laWvKq904HWbRWrUiEVqkU5JQqdMQK+TxGOMzs7CwnT54km8kQiUax221s0VF45NgxpoaG6N+5k/6REe47coRIJMLy0hKRWCzIKb+F7Cv3+uAEH0IvyzKZS3NK8E78nDIMg7HxcbKZDC6giJwaHBoiFA57wqmGQWZ5mZGxMU+lWaiRr66sMCJaWw0f/abrvO3tb+eLn/0s/QMDAW3Ad2W94fWvv+zzbDab/NEf/RFf//rX2bRp07/auvnnxMtKHDKfy/HnH/kIsqIwNDISDKbv/OEPUVWVPxbKyPV6nbm5OY4ePeoJJ4rvHxoe5tyZMzQbjeDhSHgojP7BQf74gx/kiccfR8JrSQ0OD9Npt6lWKszOzPCfPvQh/s27302n3Q5aR+DBJIvFIqFQiKiA4foikYosUxJs9FgsRnZpiWqthoQHGAgL9IiP9Oq02x67XrzG55DUa7WgXFSE6dWu3bs5d/asx9pf1z7QxU3Kb6e1Ox0U2XOWQ5Jo1Ou0hBz/zp07OX7sGKqqerauuRy9/f2Bl72PvNmydStnT5/2fFvEz+2YJnOzs5imyXve8Q62btvGdTfcwJOPP04hl8MRc61//7730dXd/azn6bouH/nIR/it3/qtVw6Tlygq5TJ//pGPYFpWoA5tWxb333svruvy+x/6EOBtUrOzszz++ONBSwdgYHDQm8/V614VLUAky8vLdPf08Gcf/jBHH3vM04VLJr12kbBQmJ+f5+d/8Rf5lfe9D0VROHf2bODYme7+/9k778AoyvSPf7YkW7KbTd/0ngAh9KpwNBHB7tn72fVQit2f/aTYBU4BvcOOiAVRBAUbUlV6CT29beputvf9/TGzY4AEQbgDPb7/iNlkMpmdZ995n+db4mk0mYS8oagofCJbMWxkamtrE8gr0dEC80kU0IZp95EqFUlJSThdLhqcTvrk5wu7JVHYq1Aqcdpsklgx7LNVVFzMgX37aGluFrJM/H7kMhlK0fsPhPs2HLmg1mgkexSv10ubxSIEym3eLJmrtjQ3S58V4ZpyuVx07daNkp07Jft/EHZFZaWlGFNSuOHKK8nNy2PYqFFs3bwZc2srwVCIzKwsbrz11g5tqACeffZZrr322lNuMYFTbIey6ocf8LjdB705SoWCtPR0vv/2Wy65/HIMMTFSf97pdLILsNpsmM1mYmJiOHPoULZt2UJzc7MUm5uckkKbxSLE3yoUaLRa2tracDgc9O3fH2VEBKb6esEIUqU6yMpEJpNR0KUL69esEQaACBTjnJwc9u7dK9wsCJkeWq2WRKMRlxjBGwyF8LjdFBUXo9FqcTockrJcrVZLN3nY0yc9M5Oa6moppMdisQhCxlAInxhC5PN6CYmhXkHxKSsgtsMUCoWU/46oCQjntYQNLx1OJ9FuN8jl+MT2laWlBYNI+6ysqCA+Pp7amhqBUafTkWg0olarKS8ro2THDu64+27yCwtRqVQkJCZ22mr56aef2L9/P3PmzPnP3jin0SnWrVmDzWaTWERhq5209HTWrV7N5VdfTWJSEhqNhqKiItxuN3v37MFut9Pa3ExsfDxnDB3K9q1baWpokD5oU9PSJEdcmUyGTq/HbrNJNRXe6Xq9XoFd6PdL8wqFQkFOQQENYvqpQi5HrdPRpUsXDuzejUqlwgs0iL5gicnJ2EUtVlC0X+pSVCTMT2pqsIht7UiV6mBKr0xGVmYmlVVVBPx+tFFRWMxmtFotgdhYidzj9XiQB4OoxahfiSQjCjelkCuREOR0udBqtcLni1KJ0+XC5XIJ5Abxv5bWVmJiYyW5gyE2ltqaGupqa4lUqTAajWi0Wmqqq9mzaxc33X473Xv0ICIigiSjsdOa2r59O2vXrmXVqlX/vZvoGHBKzVBKdu6UEsjCvX3EAXV4pwFiP7i8nAN795KZmcmEe+/FVF/Pnt27cTqd5BUU0K2oiPseeYQ5b76Jy+kkPTNTcuYNBAISkyocj6lUKoUkSIQPZkNMDD9+/z2/rF+P2+2mV9++gkajrQ21Wo3b4yElNZVu3btjTEmRxIXNjY2S3X10dDQKpRKzKJgKiowT4c+SoYyIoK6mhrq6OpqbmnA6nRQVF6PWaCAUwmG3YxEXyoysLMn2Ifz0GBMT8+tcRWx3hdko4ZZZc1MT3bp3FwSLPh9acecWFH3C6mtr2VVSws/r1tFgMtF3wADuvvdezho7lqzcXAYPGSLFHMfFxxOfkMD7b79NYlISiUlJnd74Ho+HBx54gDlz5px2Ej6J2LNrF2rRG6p9TYVbWrUiPTwUClFVWcm+3btJNhq575FHaGpuZndJCXa7ndy8PLp068b4yZOZ9/77OJ1OaZEKRyOEffjCBobKiAjJ9iUUCmFMTmbV99/z8/r12KxWevXrhwxhF6XRaHC6XMSnpJDdvTspmZkoRYPURpPpV5cGg0Gw/zGbBdJLMCgN7QmFUKhUVNfVUVVXh6m5GavVSs8ePdBqNBJJpbW1lWiD4aCaCtdPXFzcQbPK9jZHMoSWWYtYU+GdjFRTwaBgrVRfz+5du/h53Tpqa2ro2acPE+69l7Hnn09GVhZDhg1Dp9dLxpFJycksePddYkUKdmc15ff7mTx5Mq+99lqHnoenAk6ps4qNjWX/nj2/Ul7FCxvO9IjS6airrWXmCy9QWVmJQtRaDDzjDJ557jl+/O47du7YgUGv54Zbb2XI0KGUbN8uLUp6g0FiZ6nVarwIYTXJKSnCdrh7d3bu2MFL06dTUVZGm8VCa0sLtTU1GAwGCrp0wWAwcMudd7Jj2zZ++PZbEpOShOQ18TyjdDq8YkssUlTnhinDflF/sm3zZqqqqnCKxpXhcK76ujpaW1rIys6msFs3qisrpSFotMEgxKmK4T9en4/YuDisbW3STip8vcLb/EaTSWJ75eTmUlFejqWtTcqsSE1PF+zIRVdZmVxOfHw819xwAzNffJHY9j5hIjRaLRazmbIDB+jWSQIjwEsvvcSll15Kly5dTvyNchpHjdi4OLztKMG0ez9DoRBRUVE0mEz886WXOHDggFRTffr355lnn2X1jz+yfetW1Go1V19/PcNHjaJ0/36JzRWeT4Zp+DK/nzaLhbT0dDxuNz1792b3rl288txzHNi3D6vVSmtrK3U1Nej1enLz89Hp9dxyxx2Ul5Wx9IsvSDIaqSwtleYXUXo9XtE8NpwYGqYMu0UB4bYtW6iqrsZmtQrZ7WJNmRoaaDWbycrMpKCoiAaTCVdJCW0Wi8DajI3F4XAI1jLhzoDZTCBcU4Qvm/DvRlHPIlcoyM3Pp7y0FKtIrvH7fCSnpgoLi9MJovWRTq/nmhtvZN7cuUQbDIfVlEqlwuf3s3/vXnr37dvpezl37lyGDRtGn06G9acCTqkFZcRZZ7Fq5Ur8gcCvTx2AubWV9IwM4hMSuP+ee3A4HKSmpkoDxp/XrcPn8/HQY48BwtNxZWUlGzZswN9ONRvOdmgzmwUeudgGampsJBgM8sG773Jg/34aTCb0ej15+fm4XC5BzGW1kp6RwdOiIvWD996TfHWio6MFo0ex36oRzRW9Xq+QtyIWWdiUsWTnTsluP0LkwsfExKDWaKitriYlLQ2t2CILhkJSSJfeYMBpMqEAKY40Nz+f/Xv3AgJTq33IT7h3XFNVRVDU5XTv2ZP9e/eyb+9e7DYbBYWFAmtFZIrt2kAZYScAACAASURBVLmT8tJSid3TEcICr86wa9cuVqxYcVJdT/8X0dH7NXT4cL5askRgJLV7qrVYLAI7KTOThyZNwtzaelBNbd20CbfLxRNTpkiOFVVVVfzyyy/IQXJv0KjVUhJhpFhTCoWCluZmAoEAiz7+mIqyMmqrq4nS68nJzcXj8dDa2ordbicuIYHnXnmFaIOBJZ9/LrlTR+l0BAIB1FotCtHE1B8ICPn2ohO3ta1NMlzdsWOHIFQUa0qt0RBtMEj0+oTkZKJ0OpwOB4FgEJ1IN442GLDZbNKuJEKhIL+ggL179kg05lAwKHl6hXdidTU1VFdWUlBYSI9evaioqGDXzp04HQ5y8/KIEVtqHq+X8rIydpeUSPOhDt87jlxTFRUVLFiw4JSwVzkSTqleRJdu3Rh3/vnU19bS0NAg8N6rq5HL5fx94kR+Wb8ec2srie369nK5nLT0dCnzBIQVv7CwkL59+woak0CAhvp6rFYrxT16kJGdjcvtxu12I5fL0ev1gvOuVivE9Io3c1lpKbFxcfTo3ZueffpQXlYmqfTVarXECFFrNGi0Wkl3olAo6N2nj5S17XA6cbtcmM1mEhITBftpcUfh9/sxxMRgTEmRLOXDO56wBkUutiiUSiU6nU6yhAkFheRInV4v5FKLC4nf70ehVGIwGEhNTydeFCvKxCe71tZWVGo1KpWKyspKlBER6PV64uLiUCiV/LhyJWkZGYfx4AHJ6ygnL6/D9zDsevrqq6+eVNfT0xCQk5vLxZdfTkN9PQ0mk1RTAb+fe+67j21bttDY0HBQ314ul5OSmsrukhJK9+8HhIeX/Px8+vfvL9xPCgX1tbVYrVa6FBUJC4UoTlQqlUKbVKlEo9HQZrFIAuLysjKiDQaKe/akd79+1FRXS/Te9jUVqVaj1enweTzCg5FouJqUlCRoNJxO4WHPbCYuIUFqFYUV8lF6PcmpqUJrW7SOAYHWLxPrKawnizYYfq0pBDJKuKbC3+P3+YS2m8FAWkaGIFYURdeRYux3ZGQkarVaMtrUhWtKLmfN6tUkGo0Sa7M9wjOf/E4SGIPBIJMmTeLFF1/s0H7lVMIpE7AVVp5ee+ONPDFlCmcOHUpBYSFXX389z8+YQaZoZtfRh1T4RqqurJS+1tTYyP69e4nW63ns6aex2WySyj0lNZWu3brxt1tv5e0PP0QfHU1KaqqQJieaJEaI4VUmkYYcGRkpiQQB+g8axO6SEjZv3Eh1VRUDBg0SBpOiK6/D4UCn01HUvTtDhw0jEAig0+loamzEK4rH9KIWxGI2H6RUDjOvoqOjBV68OGNpEOnNMXFxQv+7tpZWMbe6S7duxCUkSFYuoWCQuPh44uLjCfj9qCIjqa2pwevzSQ6o4WyW9u7OCoWCESNHMvCMM1BrtezdswePyJO322yY6uq44tprO8xsAJg3bx79+vX7T+bDn8ZRIpyPc+kVV/DUtGn8ZcQICgoLufzqq3l+xgzJfqejCIFwfVa386dqaW5m3549aNRqHvvHP/B4vVRUVOBxu0lOSaFLt25cc8MNvLVgAQlJSRiNRrRarfAQItKB5XK5NAsN+8yFtU0DBg1i/969bN20ibKyMvoOHIjeYMBhsyEDyXW8sKiIoSNG4A8KUbvh+aNKrUan06HWaLBaLLjD+o4wyyucdxIIUF9XR1NTE02NjWRmZRGfkCBRh1vEuWph164YjUZptx4MBomJiRFqSqQR19XWChov8fMi3B1oL/SNiIxk6LBhDBg0CENsLHt375a0Jw6Hg/raWi6+7LJOs4MWLlxIeno6o0aNOr4b4r+AU6LlFb7xQfgw7VpURNeiosO+zxAbK1D9OkGUKGacM2sWv6xfj1yk9XUtKuLK66/HabOxdfNm5EolV11/PaPHjMFmtdLa0kJqWhqhUEjYCns8ggVKRISUr26zWsnNz6epsZG1q1bxxmuv0dzYiENMeauurCS/sJDMrCyuufFGtFotr7/6KukZGQLFWLTTDy824VAtpVIpLWJu8enM5/Oxc/t2IWtFzKcPhkI4nU42b9yIXq+nb//+GFNSWPnddwSDQWqqq8nNy6OlqUnKbmhtbSWmXX5CKBTCYrFQUFBAq9iSaI/K8nJCoRD33XMPUVFRDBs5ErPZLNmqpGdmcvfkyQwdPrzD619bW8tbb7112kn4FMChNVXQpQsFHcyzYmJjD7sP2iNKp8PpdPLGa6+xdtUq5OKMJb+wkL9eeSU+j4dNGzYAcOlVVzFm7Fj8fj91NTUSTdlgMGAXQ7kiIiKwWa0AOB0OjMnJOOx2vv/mG1575RWam5pwiOLi2qoq8goKSElP58prryU+IYFXZ8wgLT1d2ImLM0mlUikEwIkzF6VCgTcQQC6KCxFJAzu2baPBZBJcMcQhutvlYsumTURFRdGnTx9S0tNZvXIlPp+Pmupq8goKaDCZhN1IMIjZbCa+fQJmKITFbJY+G8I7rDBqqqvxut08/uCDaDQaho0cib2oiFXffy/oeFJTuX38eEZ1ojlpampixowZ/Pjjj6eMvcqRcNIXlPAbG+ZvHwlD/vIXFn/8saRQDcNms6HT6+navTtTnniCXTt3kpySIjGeNm3YQHNTE/984w1uvuMO/H4/NTU1bNiwgRhxUB8Q1a35hYXCoiP+bDhe2NzSgj46mluvv55dO3cSoVQSl5BAUnIy1rY2PG43Lc3NLFy8mPTMTD5duFCyKlEqlSgjIqTfIZfLiY2PxyLOctRqNW6XC6do7b27pETg5ItPRiq1WornbbNY8Ho8pIgW/IFAQIosrq+rQ6vT4RIT6cLW8/roaNra2kgyGrGKorTinj3ZvnUrwWCQNotFIgR079GDJKMRn8/HsiVLyM3P5/1PPqGtrY3GxkYUCgVtbW2CI3M7hJ2ETxXX0/91SPkev/EhNHDwYD545x28Ho+0cwXhyVmt0dCzd29eef55Nv78M8kpKVILaMe2bZjq65nz5pvceMstBAIBamtr2bRpE/HiDjogzhvyCwvZ+MsvUjKoQrw3m5ua0Gg03HbDDZRs345CqSQ+Pp5EoxFbWxtut5umhgYWLF5Mbl4eXy9dKmW7y+VyqUUW3vnExcdjNpsJiSarHrcbp+gvtnfPHoKiy7FMJhNSXNVqYsT8EbfLRarIWvN4PAIt2O+ntrqaaINBUPOLmfQAer0eS2sr8QkJWMxmYmNj6dm7N1s3b8bv92Oz2WgWd0DdiosF4o/Px9dLl5KekcHbH36Iw+HAZDIhk8kwi8c4NNjr4Ycf5vHHH+80WOtUw0mfoYRVqEez+mZkZnLdTTfRYDJRX1+PxWymrrYWt8vF5AcfpKKsjF07d5KSmiotTnbxKai5qYmtmzcDwlY7OzubgQMHoo2KIjktjUrRKj45JYVu3bvj9/txOByo1GraLBYpGyE8dJQrFDSYTERERNC1qIheffuijYqS5jiRkZHSoE8ul5OblyfNWGQyGSkpKRQUFkoDz6aGBswtLSgUCjIyMw+iBQYDAdIzMgRbcPGDwtrWJth1i7uwCKUSl8tFdnY2hEJ4RIv8oN+P1+MhKM6R9uzaxU/r1lGyYwdZOTncNWECN956K/EJCQw+80xpIVapVKSlp1O6fz8bf/6ZjIwM+vXrR25uLpWVlWzatIlmMVIZBNfTmJgYxo4de+JujtM4JoRrKBAISDX1W3WVZDRyy5130tTURH1dnVRTdpuNSQ88QGNDAxt/+UVwWBBbY06nk/jEROw2Gxt++gkQWqWZmZkMHDgQjVZLVm4ulRUVBINBEpOSKO7ZU3C0ttlQazRYWlsl9qNM/HmFQkFDQwNymYwu3brRq08fog0GKsrKAMFuqD2TMS8/XxIYh/+WLt26SUy18MxIJpOR0S69FQQDzLT0dAwxMdK1Mre2SiF5fr8fpUKBx+MhPSNDqFORYRkUyQGBQICmxkb27t7Nz+vXs23zZjIyM7nt73/npttuI8loZMDgwaSlpQnHFWuqprqaNStXkpaWRr9+/SgoKKC2tpaNGzfS2Ngo1dS3336L0+nk8k6yhU5FKJ4S1eed4IgvHg/Cu4L2XkFHg8KuXek3YAAyuRyVWs3Q4cO5ffx4snNy2LJxIxt+/lnqRYbto2NiYnDY7SQkJtKzd29AeAqpqa5GpVIxdNgwVv3wA5UVFdKsQ6fXU9yzJw+JTwe1NTUkGY3U19cLMcUyGarISGxWK0niQNBht9Orb18Ku3RBHx3N4k8+wWqxCDnQRqNkVeLz+SRvMJ1eT+8+fTDExtLS0oLP5xMys5VKKRslIG7fVSoVbSJNOCMrS3iK8vmoq6vD5XQKBpoxMRhTU2ltbhYWqqYmwQQwKgpjcrJEp5QrFOh0Op6fOZOYmBhWfvddh2r3YDCIra2NEWIIkkqlIjk5GYPBQG1tLfv27WPZsmW8+uqrLFiwoNOUxpOMp0/2CbTDU/+pA//emsrNy6P/oEEolEoiVSoGDx3KHePHk19YyI5t2/hl/XoppdHn82G324mNjRXuX52O/uK8LNwmilAq+cvw4axdvZqy0lL8oqhQr9fTtaiIhx9/nIzMTMrLy0lOTqaxsRFzSwvI5agiI7FarZLDr81qpUevXhQVF6OPjuazTz7BYjZjt9mIS0hAIZcLImaR8ut1u1FrtfTq04eEhASam5vx+3x4xYjiYChEpEolJayq1WqsVqv00Bal0xEMBqmtqZFqSqfXk5aeLv2epqYmXE4nGo2G5NRUfGGNmUKBRqPhhZkzSUlLY/nSpSS0b4+JkAGNjY2MES3nVaLQMS4ujvr6evbv38+XX37JjBkzeP/99zF0Mls5yeiwpk5qyyvcuz3W3mBufr6U+NYeGq1WOlYwGKStrU3K4wiGQhhiYgiFQnz+6acsnD9foiDm5ObywCOPUF5WxnfffIPb7eayq6/mwksuQa/X88E772AQWzxhBliYD+/xePC43WhEynCS0UiDycSbb7xBdVUVFrNZYpSkpKaS36ULZwwdyhlDhvDOvHnSMRsbGwkEg2g1GqmlF1YVhx1N3R4PyogIvF6vZA9TW1ODRzSnlMlk7N2zB7noa9ate3f8Ph8bfv5ZCDby++nRq5d0vZoaG1n1ww8UFBZ2frHFYeShCLsVNDQ08Mwzz2Cz2Vi2bBk33njjMb2Xp3Hi0H5ucqw1lZ2Tw99uvfWwr2vakS+C4gwu3Jrx+3xSTX29dCnvvfUWbpeLYDBIRmYmEyZPpqamhm+XL8fhcHDRZZdx0V//SkxMDF989hl6sTWq0+mEIbfYugrnC4WZYklGI02Njbz75ptUV1XR2tIi1JRcjjElhfzCQvoNGMDwUaP4aP587A4HBoOBqooK/H6/pNRXiiFz4ZZZSFTdRyiVuJxOAuKwvrqqCq/HI7XHDuzbJ2m0isTY6rBUweN2071nT+l6NzU18c3XXzN4yJCDND/tIeukpjQaDd26daO1tZXp06fT0tLC0qVLufPOO4/pvTyZOGkLynPPPUd6ejqXXHLJCTtmr759iRS1Hi6XS0py9Hq9yGUyBg4ezOJPPuHN118nISlJKobamhqefeYZZsyZw8WXXSakJdbXU1JSQnx8PCq1WtiVgBQSFPD7kUVGSrOf5qYmAoEAz0+ZQk11NSbRoTexSxchT9rloqmpiRtuvplb77oLU309drudZNGvR6lUShYsSqVSoCNGREgeXqrISMF+XmwR/rR2rSQoU4r0TH10NDKZjJaWFrRarVBUIvNNq9XSZjbjsNslNwK5XM6+PXsYfc45aLVaXC7XYbREp93e6RAeYM+ePeh0OjZs2MDPP/98wt7L0zh2zJo1C71ezxVXXHHC3AmKe/ZEo9EIXlY+nxTzHCbHnPmXv/DN118ze+ZM4hMSiI6OJhQK0dTYyLSnn+aV2bO54OKLCQaDNDQ0sGfPHmJjY1GJjEoAbVSU1GaKiIyUWFmtLS34vF7++fLL1NXWUlNdjdFopLBLF1paWnC7XDQ3NXHZ1Vcz8b77aLNYePXll0lOTQUQnMcR2mlhOnGkSiW1nsORDmHvuo5qKqxob25ulmYuYZaaNipKyH+32YgWd3BKhYI9u3bx1yuuEEST7eotDKvVyoWXXtrpNS8rK8Pv97N9+3ZWr159Qt7H/xZO2gzl+uuvZ/369YwcOZLPPvvsiEyTo4VOp2PCvffSYDJJN2ODyURTUxM33347MbGxfPTBByQkJUkRtTKZjLj4eNwuF199+SUgalvS0hg4cCA6nY6s3FxMJhMu8elrwODByMUMELlMhlX0BVOIOhFLayuRKhVNTU20NDeTl59P9x49yMrOZr0o9pOLT0thUoJKrZZMH0G4MXv07o1OzK+ura2lqrISl8tFZmYmKWlp0vcGg0H00dHEJyRIfkZ1tbWSMCsc7INMJrHWQOgjh11P/3bbbbQ0N0vK+7CNd3pmJkM6WVCcTiePPPIIs2fPJjExkfPPP/+438PT+P246qqr2LlzJ8OHD2fBggWHMY5+D1QqFZMfeoiWlhaam5rw+3w0NjTQ2NDA1TfcQGpaGvPffpu4uDgpl0MmExJUA34/XyxaBIjalpQUBg0ahMFgIDMnh6bGRtxuN16vl4FiVk5YzOuw2wXbezFHyNzaikqlorWlhcaGBnJycykqLiYvP5+f1qyRKLu0s1GJiIxELe74QchD6dmrF3pRiFxfX09lRQUOu520jAxS09OlaxYUnTkSEhNRqdUolUohzldMklSIhBu5TEabaC8Dgj1KUnIyCoWCW+64A4vFIpFv/H6/4O6dlMToMWM6vN5er5d7772X2bNnExcXx0UXXXTc7+F/EydthmIwGDj//PMZO3YsH3zwAdOmTSMmJobCwsLjeroyxMaSkJRERkYGoVCIHr17c8f48Qw680yqKiv5asmSDnuSMpkMi9nMuAsukL7msNtRyOUMHDyYHdu2CewuUYWr0+mIiY3ljrvvZvQ557Brxw4yMjMJBoNUlJdLQkOn04lezBYJ35Q33HIL2qgofl63jprqalqam9Hp9aSkpAjWEE6n8FQjGjymZ2bSvbgYt8slmNM5ncTGxdHW1ibZu7jdbvR6PYFAQLLnNiYnC9YajY1CnopIi9ZHR+N0OGhuaqK5uZlPP/oIhVzO6LFjaaivp7qqCr/Px9njxnH35MlS//xQPPPMMwwdOpRzzz33d79fYQQCAfr168eXX37JNddcc9zHOwT/EzMUvV7P2LFjueiii1i0aBFPPfWUFMp0PDUVbTAQn5hIdk4OwWCQou7duX38eP4yYgQNJhOfffKJ5BrRHgqFApPJxEV//av0NaeY2Dhg0CD27t3Lti1bUIpmp3qdjmiDgVvvuotzL7iAndu3k5GRgUKh4MC+fahUKpQKBS5xdqNSqSRj1xtuuQW1RsPO7dspPXAAi9ksRDikp2Oqr8fpdBIVFSWlkKZlZNBdJN+4XC5cDgeGmBhpEQvXlE6vlxIeQRj8hwP8/H4/hEJotFoMMTG43W4axRykTxcuxOvxMPb882lpbqaqogKPx8PI0aOZeP/9HV4vgJdffpnCwkKuuOKK3/1+hXEyauqk04YzMzOZM2cOVVVVTJs2jZdffpl7772XCy+8sEPB1ZHg9/spKSlh5FlndTgc1mg0ndIp/X6/9DOVFRX8a/ZsiRWWnpHBkOHDGXTmmaz58UeampoYeMYZXH399WTn5LBsyRIpk0VKVmxn1Gi32YiKisLtdpOUnCyp4dva2gQOfyiEMiICg2hYFxUVxZhzz8XtdLLiq68klonNZpN2VvV1dWg0Gjxut+QyHLa0D+eh2Gw2EhMT6dW7N1s3bRLcUQFTfT31dXUkJCaiVChQKJX88N13rFm1iukvvURmdraQ/X2E679t2zbWrVt3wlxPZ86cSbdu3bCKGoXT+P1ISUlhxowZ1NXV8dxzz/Hqq69yzz33cNlllx2zqWAgEKCkpIS/DB8utXXaQyUOuMO29u3h9/slgkxdbS3/mj2bjWJbNCk5meGjR9OnXz/WrlpFg8lEr/79ueb668kvKOD7b76RTCxDoiV9mHYPgi5ML4bHxcXHI5fLsba10drSwtZNmwTKv+gAkZ6RgUarZcy4cQQCAb5asuTXmrJahYcymUzIWxGTK5VKJaF2NRUQdWR2u52EhAR69enD5o0bcYuizQaTifq6OmJjYwWZgFLJutWrWbdmDVOef56CLl2EhMgj1NS+fftYsmTJCdNxnYyaOum04TAyMzOZO3cun332GatXr2bEiBHH3ArbK7oPd8Y0Sk5JISc3F3O7LSoIw0y73c45551HfV0dD0yYwI5t20gyGjEmJ2Oqq+PN118nJyeHV994gw8XLeLWu+6isamJkpKSg3yv5HI56enpkuGjjF+HcK0tLUQolZx/1lmc0bs3a1evJjklhYSkJMHy2mLB43bz5vz53Hn33ZIyOHzsSNEhOVxkCQkJknGmDMECRq1WI5fJsNvt7Ni6lR+//569u3eTkp7OkOHDOXvcOIp79SIzO5vuPXqg0WqJjIyUshfmvvoqarX6iDd+2PV09uzZJ8T1tKamhqVLl3JrB0Ph0/j9SE1NZebMmSxdupRt27YxfPhwPvzww2Nqhe3fv5/k5OQOFxOAuPh4uvfsSUtLy0FfD4VCtLW1Mfb882luauL+e+5h84YNJBqNJCUnY25tZd6cOSQmJjJzzhwWLFrE+IkTMVss7NixA5/Ph7wdRTgjM1NiKAKSG0RzczNROh3njx7NoF69+HbFCpJSUkg0GlEoFNKM4/W33uLvEydKui+ppsRoB5nYgo6NiwOZjIA4NFeLFkUKuRy3y8WOrVtZtXIlO7dvJzklhTOGDmXMuHH07tuXjIwMinv1QivWVDi47tVXXpFEzZ0hrOOaMWOG9NB4PDhZNXXKLChhhBeWRYsWsXr1akaOHMnixYt/c2Gpr68nFAqRKg7kOoJMJuPuyZNRyOWY6utxOBy0WSzU1dbSf+BAhgwbxmcff4zL6SQxKUnaacgUChITE5k3d660u0lMTGTAgAEYjUZUGg1Op1OKGM7Nzyc2Lg6PxyM5D5vq6vCKpngRkZH4vF5cDgetzc0YjUaKiovp2bs3wWCQkh07Ojz/9IwM/KJyHoRWRGZ2tqCmR1C6V1ZUIFcopNmI3+/HbDbjsNl4fsYMHn/mGbweD0aj8bDjx8bFsX/vXsl6ojPMnj2bkSNH0lukYB8vJk2axPPPP3/a5v4/hPYLy9atWxkxYgQLFy78zYWlsbERj8dDhij46wx33nMPKpUKU10dDrsda1sbdbW1dO/Rg1Fnn82yJUuwtrWRKFKBQ2KXICU1lXlz50oPZPHx8fTv35+0tDRUWi0Oh0NqNWXn5JCYlCTUlNdLwO/HZDLhdrmwiOF3HpcLt8tFa1MTCYmJUk0plUq2b9vW4bmnZ2RI2ScgzGFzcnOFzxvR0r+yogJkMhKTklCJYso2i4W2tjamPP88T0yZAkBCB3EO0dHR1NXWSoP8zvDOO+9QVFTEkCFDjvh9R4uTVVOnbAVnZWUxd+5cPv30U1atWsWoUaP4/PPPO6TbORwOKisr6dq1628eN6+ggBmzZ3PRpZcKufE5Odz70EP831NPERERwfo1azCI/c3wU1a0Xo8+Opr6+nqam5qkY8lkMhISEjh7zBiuvPZaKsrLqaquxuP1kpefT0ZmJqNGj+aWO+6gZ9++pGdmkmQ0So6nKrVasJlvaJCOGQgE2LVzJyA4xYYpwX6/n7T0dOITEvC43fj9fnyiUj4qKoqMzEy6FRVJDDSf10th16707N2bLt264Q8EePONNySdS0c3mmSudwR7m4qKChYuXMgTTzzxm9f6aPDll1+SlJREv379TsjxTqNzpKamMmvWLL788ku2bNnCiBEj+OijjzpcWFwuF6WlpRQVFf0mBTkjM5OZc+dy2dVXExsXR3pGBhPuu4+np09HrVazdtWqX32qQiHarFb0ej06nQ6r1XrQh61MJiMuLo4RI0Zw0623Ul1VRWVlJW6Ph+zcXDIyMxk2YgQ33XEHg844g7SMDJJTUgTrlVBIEhQ31NdLxwyFQmzfsgWAM4YOlerG7/djTE7GaDTicbvx+f34AwHJ8ysjK4uuRUWoxdA9j8dDQWEhPXv3lqyh/j13rkDj/42aOpJllMlk4vXXX2fatGknxF7lZNbUSZ+h/BbCC0tlZeVBM5YLLrhA2kGUlJTQvXv3o26/GFNSuOm227jpttsOey0iIkIyq3M4HIL/kEgPDluxhEIhvl2+nI8++ECym7/sqquY9sILLJw/n/379pFoNPLwk08yZuxYPB4Pn370kdRWat8ek8lkWCwWIQBMfC2secnOzaWtrY1tW7cKuQo6nZBfrVaTnZtLeno6u0tKSDIaSUhMFBhawSAakQJsbm0lITERtVpNfHw8WzZuZOPGjeSLCZRhr6Uw7Ha7EJzVwe4FhG35xIkTeemll06Y6+natWv54osvWLZsGW63G6vVynXXXcf7779/Qo5/GocjvLDU1tby7LPPMmvWLCZMmMCll14qmRuWlJTQrVu3g9TlR0JCYiLX/e1vXPe3vx32WlhTBcJQXikKKA+tqR9/+IGF771HZUUFiUYjl111Fc/PmMGH8+ezZ9cuYuPiuO+RRzj3gguQyWRccPbZknAwTP8NK+7b5wSFQiHJ0y4zOxuXy8WKr78WbPGjokhJTSUtM5PMrCwpYTE2Ph5jcjJOh0MQZmq1eDweWpqbSUpORqVWk5iUxLbNm9m8eTO5BQX8sGLFYRRhl0gGSO9klxcKhbj//vt55plnTpiA8WTW1Cm7QzkUWVlZvP7663z88cesXLmSkSNH8sUXX/DCCy8QExODXq8/Ib/nrHPOwWw24xOfOsLzGHNrK127dSM2Lo43Xn2VF6ZOpbWlhfjERCwWC6889xybNmxg5ty5LPv+e2bOmUNScjLbtm2TNCzhmztCTjf9gAAAIABJREFUZKuEwgl6Inyi8+qaH3/kqosv5ryzzsJqtZIozlhsNhtlBw5w6ZVX8u7ChUx84AG8Ph/xCQmAkC0fXqwUCoVkgw+CKC02NpZBgwZxwSWX4Pf7qayokIaONqsVq8XCzbff3uk2ecGCBWRlZTFy5MgTcq0Bpk+fTk1NDRUVFXz44YeMGjXq9GLyX0JaWhr//Oc/WbJkCZs2bWLEiBF8/PHHUh//UL+234vR55yDzWrF7/NJ6noQkhrTxR3G+2+/zbQnn6ShoYH4xEQcDgf/fOklvv/mG16cNYul333H7HnzSM/KYuvWrVgsFimBlVAIRUSEYItySAfD7/fj8XjYtmUL11xyCWOGDqWpqQmj0YhCqcRut1N24ABjzz2X9z76iIcffxy7zUaS+FDl9XolgaJSqZRs8EF4wIqOjmbQoEGcd+GFIJNRXlYm+X057HZaW1u5+fbbO33Y/fLLL4mIiODCCy88IdcaTm5N/WEWlDCys7OlheWtt97i/fffZ/fu3UcMpzkWXHDxxaSmpVFeVoZK1IU0mkwA3HH33dRUV7P4009JSk5GJ+ao6HQ6kpKTWfrFF1SUlwNCPG+fPn3Iycmhrr6e1PR0mhoacNjt6HU6uhQV4ff7cYpiwtbmZqoqKwmJmRFVFRVYzGbazGYiIyPpWlREcY8eZOfmsnzpUqnlJW+XASGXy9FERQmiy3Zq3LD3l1yh4MFJk1i/ejVPTZtGcc+eVJSVUVZaSpROx+PPPMOQYcM6vC6NjY3MmjWLF1544Q/henoaR4/wwvLFF1+waNEi3njjDXbt2nVCtGEAZ48bR3ZuLqWlpYInnt9PU2MjPp+P8ZMm0dzUxAfvvkui0Yg+Ohq5XC5YBaWk8MN337F3925AmEf07t2bgoICqquryc7Lo8FkwuFwoNFoKCouJiBGW6s1GsytrVRVVgrZRgcOUF1djbm1FavFIkU+FPfoQW5BAd+tWCG5SYQXkLAnWli3IoNf02QBi9lMlE7HAxMm8N3y5Tz+j3/Qd8AAKisqKCstRalU8tBjjzG6E3+7trY2pkyZwsyZM/80NfWHW1DCiIuLo7GxkU8//ZQffviBUaNGsWTJkuNeWKINBm4dP57Lr7kGpVKJx+1m+KhRzJgzh4IuXdiycaO0tW6PcKtg4yFqcYPBQO/evbl70iScTicmk0kamufm55OUlESvPn0YPHQocfHxFHbtSmxcHBaLRdCuiDx7v88HMhlqjQab1UpFWRkpqalEGww4HA6sViuG6Gi6FhURAtwuF9qoKJxOpxRnXF5Wxv69e1n08cf849FH+evll/PFihW88c473DFhAlHR0djb5TiEEXY9feKJJ4jthD9/IjBixAi+FMWlp/HfR3x8PNXV1Xz22WfSjuWTTz457oUlKiqKW+66i6uuuw61SoXL6eTMoUN5+dVXKe7Zk22bNwvU+UOe4sNsxvWHJH/q9Xp69uzJ3ydMEKzy6+pQKBQkJCSQV1BAYlISvfr2ZfCQIcTGxAhZQfHxWCwWlBERKCMiaGxsFHYSovOw1+tl3+7dxMXHk5ycjMNmwyrOerp064ZMJsPldqONisLldFJZXo65tZXK8nL279vHks8+48n/+z/OPuccqabueeABYuLjO6TthkIhnnjiCSZNmiS5Zfwn8N+uqVN+htIZoqOj+fHHH4mOjuaNN96grKyM6dOn89JLL3Hfffdx3nnn/S6Gg8lkIioqinsmT4bJkw97Paxj6QzhBc1us7F82TJ++PZbZDIZI0aP5pa77hIMLEWH1qHDh3Pb3/9OVnY2C+fPZ+vmzVJRyUTFb/i/drudmNhYoe+MYCuhUCi44pprePzhh3GJAUMpKSlk5+RgNptJTExEGxWFRaWisGtXae4RHR2Ny+Xi2Wee4cPPPiO/oEDQxpjN7Nu3D7lcTk5OjtTT/Sbsb3bZZcd8PU/jjwO1Ws3KlSuJjo6mX79+1NTUMH36dGbOnMnEiRO55JJLjlkbBoK/lVyh4O8TJjB+4sTDXg+Ks5SOIJPJpAXN4XDw7fLlfLd8OYFAgOGjRnHLXXexddMmfhZtUwYNGcKd48eTm5/P0i++YOumTdIcKLwLCNeUzWY7yBBVJs40r77hBilqXKVSkZScTE5uLi3NzSQmJaHVaomIiKBn795SSzw6OhqPx8PLzz3H/E8+IS8/nzyEqOXS0lJCoRA5OTnSA9m6desoLy/n9ddfP+breSrjD7ugAAdx43Nzc/nXv/510MJy//33c+655x71wuJyuaioqDgiO6JH797CAFDUg4QRCoVQyOX07tsXc2srE++8k7raWinZ8JXnniMjK4u5b74phRZVVVXR2NQkZV233/QmGY2Ul5URLt/wVtspEgUWzp+P3Wrl+2+/lVhfHouFNjFm+L1PPqG4Rw8+/vBD3njttcOG6Bpxp/PLTz8x6uyzJXZNnKjALy8vx+PxYDKZmD59OsuWLfvTbMtPo3O0r6n09HRee+01aWGZNWsWEydO5OKLLz7qhcXj8XDgwAH69evX6f3To1cvaeFof9ywjmvA4MHYbTYmjx9PWWkpWtEE9p8vv0xScjJvvP02hpgYnE4nNTU1NLe2omtsPCxu12g0SsP6EL8+/IW/b8nixXz4/vv8+P33uN1uAn4/VrebNouFmNhY3l6wgH4DB7JsyRJeee65w/RuKpUKc2srP61bx7mi40a49W2z2SgvL2ffvn3U1dXx4osv8tFHH/3pqPJ/rr+GXxeWDz/8kOXLlzNq1Ci+/PLL32yFhUIhSkpK6Nq16xGZLbl5eYwcPZqG+nrcbjcgFI2pro4zhg6loEsX3v73v6mrrcWYnIw+OhqVWk1sXByNJhPz33kHpVJJdHQ0xcXFkmOvIjISfzs+fEpqqhCcJToih6OCa6urcdjt/PDNNyz6+GMpZTEvP5/8wkIKxfNf+e23AFhaWyWB2KEIBoNCeuQhCLfpjEYjL7/8Mm63m5KSkqO6/qfx50N4Yfnss89Yv349I0aMYNGiRb/ZCguFQuzatYuCgoIOo7vDSElN5byLLqJR9MsD8Ho8mOrr6d2vHz1792bB++9TVloqiSzVajWGmBjazGbefP11qaaKioro0aOHEAURCBy0+0kSrYh8Xi+hYBC1mHVUVVmJx+3m+xUrWPzJJ5Tu308gECAnL0+qKbVGw4qvvwYElX5nO6rwvPJQhNt0WVlZzJ49G7PZzK5du47q+v+R8KdbUMLIzc1l3rx5LFiwgOXLl3PWWWexdOnSTheW8vJyYmNjj4rZct8jj3DT7bcT8PtpMJnweb3ccMstPPLkk4RCIVYsWyYxr8If2jqdjrj4eMGmpd3NqNVq6d69O3+97DJS09IoKy0VqJURERT37Em0wUCUTofX4xE8lRIShFx50ck0UqWS0iIjRKZLlE7H559+CtBhlDKIT39yOTm5uZ3+nU1NTWi1WpYsWUJVu2zx0/jfREZGBrNnz2bx4sWsW7fuN41dq6ur0Wq1JIi1cCTcNWECd95zDzKgwWTC7XZz5bXX8uTUqchkMpYuXiy1i8LOFjqdjviEBL5bseIgLU3YBv6iSy4hNz+fsgMHcDocKBQKinv2JCY2Fo2YJRRtMBAfH09eQQHxCQlYrVbBkdjrpamxUaqp6Oholn7+OaFQiNz8/E53aHKFgryCgk7/ztbWVgKBAN9++y21vyF2/CNCdqR5AHDEF/9IKC0tZerUqZSUlHD//fczbtw4abvZ1tbGvn376Nev3zFtQcOMEo1WK91gXq+XcSNGCJoT0StIpVajErUsDSYT365dK/kIhYO90jIy6Ne/P+/Mm8c3y5cLuxKVivMvuojbx49Ho9Fw6XnnERSfrNxuNxt//pmIyEhkCM7BhV26SK0Dl9PJ2i1b8Hq93Hb99TQ2NhIfHy9RlxsbG+narRsz587tsBXh9XoZPXo07777LkWdLEp/IJxKvbo/TU1VV1czbdo0NmzYwOTJkw/y37Pb7ZSUlNC/f/9jmrsEg0GcYnhV+58b85e/EJ+QIIRu2e1EKJWo1WrhXm5o4ItvvkGr1WK321m9ciXlpaUkp6QwYPBgFrz3Hl9/+aVkXz9m3Dj+PnEiOp2O66+4gjaLRdi5+P38tGYNkSqVUFOBAAVdukjkgNbWVn7ZsYNQKMRdN91EZWUliYmJUk01NTWRlZXFnLfe6vBvDgQCjB07lpkzZ9K/f//jvv4nGR3W1B96hnIsyMvL480335QWlhdffJEHHniAQYMGsWbNGs4+++xj7mcqFAp0h+hfIiMjyS8ooK6uTrgxxWRHEBau7j16IJPJ2LFtGw/fey8up1MwlkTwFZr6wgvcc++9NDQ0YHc48Hg8WCwWqT+bmJQEgNvtRq3R4Pf5kIualrCQy263k5efz5LFizEYDDz97LO8/Oyz7N29W4oM7jdgAA899linfe0ZM2ZwwQUXHPdi4na7GTZsGB4xsOiyyy7j6adPJfPf0/i9yMjIkIxdp0+fziuvvMLkyZM566yz+PbbbxkzZswxD/HDNPxDUdyzJ3t370YbFUVIbAGDsHBlZmej0WjYt2cP90+YgM1qlai/EUolT06dyt8nTMBkMmG323F7PLS1taHVamltbkYrzkJcTidRej0el0six4RnpXa7nYzMTJYtWUJUVBSPT5nCzBdfZMfWrVJN9ejVi0eefLLTv/lf//oXgwcPPu7F5FSuqf+ZHcqhOHDgAFOnTmXt2rWMHTuWadOmnbAB2U9r1/J/99+PTExwlMtkOJ1ObFYrz82YQVFxMVdceCEBv/+gBcnpcOAPBPjo888PijGurKykpaWFV196CbOYxR0SMyP2790LMhkRSiW5BQXYbTZMdXWSmlcuk6FWq3ly6lSSU1NpaW4mJTX1MJV8e+zbt49bb72VtWvXHrdRXSgUklp+Pp+PoUOHMnPmTAYPHnxcxz1GnN6h/BcQdgz/4YcfGDp0KDNmzPhdrLCOsG3LFu67+26CoRBG0RPM5XLR1tbG09OmMejMM7nq4otx2O2/2rwgEG08bjcfLFokqep9Pp9AiGls5M05c6iuqkKj0RAIBPB6POwuKUEmlyOXycgvLMTpdFJXW0t8QoJggS9Gcz/69NPk5OXR2NBAktFIhuh20RGqq6u54oorWLdu3XHHZJ/KNfWnnaH8FvLz8xk3bhyFhYVYrVZGjx7NV199dUIEkoPOPJMrrrtOeAJqaaG5uRmFQsHjzzxDvwED+GntWhx2+2G7G21UFB63m9UrV0pfi4yMpKCggL59+3L+xRfT1NSEzW5Ho9GQmJREdm4uITEMyGa1YjGbiY+PJy09ncTERKlN8PhDD6FSqeg3YMARF5NAIMCkSZOYOXPmCXE9lclk0hOnTzTKPM0W+3MiMzOTiy66iIyMDJRK5VEbux4NevbuzfW33IJer5dqKhQM8uCjjzJ0+HA2b9yIxWw+aDEBYZ7i8/n4bsUK6WsRERHk5eXRv39/LrjkEilKQqPREBsXR15+PsFAgCidDrvdjrmlhdiYGDKzskgQayoiIoKnH30Uv99PvwEDjriYhJ2En3/++eNeTODUrqn/mZZXR8jJyeH9998nJiaG/fv3M3XqVF544QUeeOABzjnnnN+9Y6mpqWHk6NHccvvtlJeWIpPJyMnLk7bRUjhPBwiFQpjaGdv5fD52bt+Ox+NhzNixVJSXs+yLL6iqrCQyMpJIlYonp03jjCFDaDCZePrRR4lPSDjoBtNotTgdDr78/HNuu+uuI577O++8Q3Fx8QlzPYVfg34OHDjA+PHjGTRo0Ak79mmcWkhNTeWDDz4gKSmJyspKpk+ffpD/3u/dsZhMJvoPGsS1N9xAeWkpwWCQ3Px8iZHZ3NQkRfkeCrlcTl1NjfT/fr+fkh07JIHltX/7G4sWLqS6qoqIiAgiIiN5+PHHGT56NJbWVh594AHixPljGGqNBrvdzuJPPmHygw8e8dw//fRTEhISGNNJSuPvwalaU//TC8qAAQOkfxcUFPD2229LC0t4xjJmzJhjWlgcDgd1dXXSMLKwAwfklNRUlJ1Qk+VyOZnZ2QCsXrmS6U8/LVEpPR4P55x7LktWrGDbli00NDQQEx9PYWEh6enpWMxmIZu+g6eViMhI9okWFp2hvr6eN954gzWHKJOPFwqFQvJfuuSSS9i5cyfFxcUn9HecxqmBXr16Sf9ub+wanrG0N3Y9WrjdbiorK+nfvz9KpZKCLl0O+56U1NRO+5pBkZkF8MtPP/HMY49hs9mQyWR4PB6GjRzJFytWsH3bNkz19egNBgq7dCEjI4Md27ahFBMcD4VanNscCS0tLbz44ov88MMPJ3QXcarW1P9sy6szhBeWd999l88//5yzzz6br7/++qhaYcFgkF27dtGtW7cjPokNPOMM4kUriPawtrWh1+sZOnw4e3bt4vEHHyQYChETGyvECKtUrPjqKz77+GOGjRzJ5VddxciRIwmFQvzyyy+4PJ6Dsh3aw+v1knaEXIuw6+nUqVM7DVM6XsTExDBy5Ei+Fvn8p/G/gfDC8sknn0jGrp1FURyKsD6si5h42Bl69+1LWno6rYcEfdltNjQaDSNHj6a8rIyHJk/G4/USGxcnaMRUKlavXMm78+YxdNgwLrvySkaffTYKhYINGzbgcDrxiwaqh8Lj8ZCWnn7Ec3/00Ud5+OGHj4o6/XtwqtXU6QWlExQUFPDOO+8ctLAsF+m8naGiooL4+Pjf/ECOiIjghVmziImJoaW5mcaGBpqbm9FotbwwaxYajYb577xDKBRCo9FIQzi9Xk9MbCzz331XsthXKpVkZ2czcOBA8gsK0EVH09jQcFABeEUX4vMvuqjTc1qyZIlAUz7//GO8UkdGU1OTtHC6XC6++eabo8qtOY0/HzpzDD9STVVXV6PX63/TQ04ul/PcjBkYk5OlmmppbkapVPL8jBlEGwx8vGABATHqOxQKCTnzoj5s0ccfY7fZAOHpPzMzk0GDBpGVnU2i0YhJDPALw+/3EwoGuejSSzs9p5UrV9LS0sLVV199jFfqyDiVa+p/luV1rNi3bx9Tpkxh//79PPjgg4fRjK1WK3v37j0mLYvf72fjL7/QUF9PYlIS/QcNkhTFl4wbh8fjQaVS4Xa7pZwTEPyB3l6wgCyxNQbCrEWhUFBVUcH4226TRFkKpRKFXM49993HXy+/vMPzsFgsnHPOOXzzzTcn3Khu+/bt3HjjjZJz6xVXXHHCwrmOAafGxFLA6ZoSUVFRwbRp09iyZQv33Xcf559//kG143A42Llz5zFpWQKBAJs3bqSupoa4+HgGnnGGRC659rLLaGluRqPRSMF1Ycpwm8XC7HnzDhIC+3w+5HI5DSYTd918M/X19SgVCikb/ta77uLaG2/s8DycTiejR4/m888/Jysr6/deog5xKtfU6QXlGBFeWA4cOMCDDz7I6NGjCYVCbNy4keLi4hPC4gC46ZprqKmqQiNGoer1esmS3tzayuKvvyYuPp5NGzYwe+ZMdu3YgUKp5KwxY7jpttvYs2sXP69fTxAYM24cAwYO7ND+IhQKMXHiRM4880xuvvnmE3LupyBOLyinMMILy9atWyVjV4CNGzfSrVu3E5Z1dM/tt1Oycyc6nU6IkYiOlkSJrS0tfLh4MalpaWzfsoXZM2eybcsWZHI5I886i5vvvJPy0lLWrVlDIBBg1JgxDG63WB2Kxx57jMzMTCZNmnRCzv0UxOkF5URi7969TJkyhbKyMpKTk7n88stPaEjOl4sX8+w//oFKrUYbFXUQQ6z/wIG8/NprrFu9mgcnTkSuVKLX6wkGg1jMZuLi43l7wQISk5IIBoOYTCaqqqqIi4sjKyvroCJYs2YNL7zwAsuXL//TGdW1w+kF5Q+AsLHrtm3byMvLY9SoUVx77bUn7Pjff/MNTzz8MCq1Go1GIzHEzC0tdCkq4vW332bzxo1MvPNOkMmIjo4mGAzSZrGg1+t5+8MPSUlNFRwvGhqoqKggJiaG7Oxs1KLQEmDLli089NBDrFy58qhTZP+A6LCmFE899dSRfuiIL/4vIyEhgb/+9a9ERkby3nvvsXv3blJSUsjJyTkhbI7c/Hy2bNpERXk5wWAQn9eL3W4nPiGB6S+9hDYqisl//zten0/avcjlckn9q4yMZMCgQchkMvR6Pampqfh8Pvbs2SOJovx+PzfccANvv/32f2xoeIrg1JARC3jqZJ/AqYrY2FguvPBCEhMTmTNnDvv37ychIYH8/PwTUlNZOTns2b2bfXv2EAyF8Hm92Gw2DAYD019+GYPBwEOTJmG1Wok2GKSa0mi1tFkseDwehg4bJulA0tLSCAaD7N27F5vNJmlDrrvuOl5//XVSUlKO+5xPYXRYU6d3KMeJu+++m4ceegin08mUKVMoLy/nwQcfZNSoUcf1xO9wONi2bRsBn48Vy5bhsNsZMmwY55x7Ljq9ntqaGq686CIMMTGHFZvb7Uav1/PZV18ddtywj9e8efNYtWoVQ4YMYfr06b/7PP8gOL1D+QPh/vvv54477kChUDB16lR27tx5mP/e74Hb7WbLli0E/X5WLFuGzWpl0JlnMva88zDExGBubeW8s84iNi7usJryer3IgOWrVh123LCP1/z581m+fDnFxcXMnj37d5/nHwSnW17/DezZs4epU6ce18ISCoXYtGkTBQUFUsjVoairreWKCy/seEFxuYg2GFi0bFmnv2P79u3cddddqFQq7rjjDm666aZjOsc/GE4vKH9glJaWMm3atONaWEKhEFu2bCE7O5u4uLgOv+c3FxSZjOU//tjp7zhw4ADXXXcder2eq666invuueeYzvEPhtPWK/8NdO3alffee49///vffPTRR4wdO5bvvvvumCxdqqqqMBgMnS4mIAi5UlJTcXaQZ+JwOBgzblynP+v3+7nvvvv497//zfr167m8E/bXaZzGqYC8vDzmzZvHBx98wFdffcXo0aNZtmzZMdVUXV0dWq2208UEIDYujoIuXbCJ9OH2sFmtjD7nnE5/NhgMcu+99zJr1izWrFnD3/72t6M+tz8T/hALys0330xSUtIpoQQ9WnTt2pX333+ff//73yxcuPCoFxaHw4HJZCIvL++I3yeTybj///4Pr9dLW1sbwWAQv99PS3MzCYmJXH4E7vu//vUvzjjjDClFryN316NBdXU1I0eOpKioiO7duzNz5szfdZzT+O/jj1hTYcfw+fPns2zZsqNeWNxuN9XV1eSLavkj4d6HHiIkkluCwSCBQIDWlv9v795jojrTMIA/B9QskbYkRupKG6mIZlDuFMULQitqAGWRCJiqXS0xTS8bqiZuVWKrsySSkoJWLakEEy6JggHvlyhYrSUVECsoYlSQmsFiYAvIOmTgvPtHZQSqMPcz58z7SyZk9MyZd+A8eec7c+b72uHm5oa1I4ziCwsL4eXlhYULF+o/tzSF3DMli1Nely9fhqurK9auXYv6+nqpyzFJQ0MD1Go1WlpasGXLFkRERPxl2G7Iqa7hfq2txYE9e/Dr9esYO24clsbGIuXjj+H+5psv3f63335DUlISfv75Z/3yxKZqbW1Fa2srgoKC0N3djeDgYJSVldnj+il8ymsYJWRqYMbwhoaGV86/R0S4ceMGpkyZMuLoZLBbdXXI2bcPVZWVcB4zBouWLMGGTz995aSqv//+O+Li4vDjjz+O+gXM0cg9U7JoKMCf16rHxsbK9uAfMFJjaWlpQW9vL7xHWPHtVURRhCAII14NI4oiVq5ciS+++MKiE9UNiIuLw2effYaoqCiL79tM3FBeQimZGqmxaDQadHZ2QqVSGb1fQzJFRFi3bh2SkpKQMMK35k0lt0zJ4pSXkqhUKhQWFiInJwdFRUWIjo5GRUUFGhoaUF9fj6kjLMk7Eicnp1EvrSwpKYG7u7tVDs7m5mbU1tbazaynzHFMmzYNeXl5yM/Px4kTJ/Tz7z148ABVVVUmvUEDDMvUuXPn0NfXhxUrVpj0HCORY6a4oUjEx8cHRUVF+P7771FQUID4+Hg8fPjQausatLe3IzMzE5mZmRZ/jqdPnyIhIQFZWVlWm1iSsdEMn9h12bJlaGxstNoXdru7u/HVV1/hu+++40w9xw1FYj4+Ppg7dy6ioqJQXV2NmJgYXLp0ySILfQ0gImzduhVffvmlxb/AqNPpkJCQgA8++MAq79IYM5a3tzfee+89hIWFoampyaCJXU3x9ddf45NPPsHkyZMtul85Z4obih2YNWsW9u3bh6KiIuzfvx/5+fn6xjLKZ1wGqaioQEdHB5KTky1Q7QtEhI8++ggqlQobN2606L4ZM4eXlxdycnL0M4aXlZVh8eLFFmssv/zyC27fvo0NGzZYoNoXZJ8pIhrpZheSk5Np0qRJNGbMGPLw8KCDBw9KXZJViaJI9fX1lJSUROHh4XT69Gl6+vQp9fT0GH1ra2sjf39/evjwocXrvHLlCgEgX19f8vf3J39/fzp16pTFn8cCRjvObXmzC46WKSKixsZGWrNmDYWFhVFpaSl1d3eblKmOjg4KDg6mhoYGi9co90zJ4uA3V0tLC0VERJBKpSIfHx/KysqSuiSDiKJIdXV1ZjWW1NRUu3m9Go2GYmJijHrMpk2b6OLFi+Y+tdRNhDNlR8xtLGlpabRz506pXwYR2V+mHOLg12g0VFNTQ0REXV1d5O3tTbdu3ZK4KsMNNJbExERauHAhnTlzxqDG8tNPP9H8+fOpr69P6pdARESbN2+msrIyox7T3NxMUVFR5j611E2EM2WH7ty5Q6tXr6a5c+dSWVmZQY2lpqaGQkJCqLe3V+ryicj+MuUQB/9wy5cvp/Pnz0tdhtGMaSx//PEHhYaG0s2bN21eZ1paGn377bf6+1u3bqWsrCx65513SKvVEhFRXl4excXF0aJFi2jKlCm0d+9eyszMpICAAJo9eza1t7frHx+gehzRAAAFoElEQVQUFEStra3mlCR1E+FM2TFDG0tXVxeFh4dTZWWlzWuUS6Yc7uBvamqit99+mzo7O6UuxWSiKNLNmzdp5cqVFBER8dLGolaradu2bZLU19TURIGBgURE1N/fT1OnTqXq6moKCgrSb5OXl0deXl7U1dVFbW1t9Prrr9OBAweIiCg1NXVIeFJSUqikpMSckqRuIpwpGRhoLPPmzaNjx479pbFkZ2fT559/LkltcsmUQ13lJddru4cTBAG+vr44fPgw9uzZg9zcXMTGxuLy5csgIty/fx9Hjx7F9u3bJanP09MTEyZMQG1tLc6fP4/AwED09vZi4sSJQ7aLjIzEa6+9hokTJ+KNN97AsmXLAAC+vr5obm7Wb+fu7g6NRmPLl8AMpJRMAcCMGTP0E7sWFxdj6dKluHDhAkRRhEajQW5uLtLT0yWpTS6ZUuxyYsPJ+druVxloLEeOHEFdXR127dqF3bt3o6urC1lZWUNWkbO1lJQUHDp0CI8fP8b69evh4uICrVY7ZJvBK0c6OTnp7zs5OaGvr0//f1qtFi4uLrYpnBlMiZkCXswYfufOHajVamRkZEAURaSnp5s8kaolyCFTDjFCIZL5td2jEAQBfn5+OHLkCNLT06FSqbBgwQJJa4qPj8fZs2dRVVWFJUuWYPr06UPeIRnj7t27spoV1xEoPVPAixnD9+/fDw8PD0RHR0tajxwy5RAN5erVq8jPz0d5eTkCAgIQEBCA0yMsPiVXgiAgLCwMBQUFZk8FYe705uPGjUNkZCQSExPh7OyM8ePHw8vLC/fu3TNqPzqdDvfu3UNISIhJdTDrcJRMAYCfnx+Ki4s5UwaQzWzDzLbMnd5cFEUEBQWhuLhYPzlfaWkpampqoFarDd5PaWkprl+/jl27dhldwyA82zCTnCNkyiFGKJag1WoRGhoKf39/zJw5Ezt27JC6JKsKDw83eP2I4W7fvo1p06bh/fffHzLTa3x8PDw9PY3a18Dqkkx5OFOGk0umeIRiICJCT08PXF1dodPpMH/+fGRnZ2POnDlSl2Y1SlkvAzxCsUucKVnjEYo5Bi+Vq9PpoNPprDbVPGOOgDOlPNxQjNDf34+AgAD9IlVyWviGMXvEmVIWbihGcHZ2xo0bN/Do0SNcu3ZNCcNWxiTFmVIWbigmcHNzQ2RkJM6ePSt1KVazatUqhIWFobGxEW+99RZyc3OlLokpGGdKGfhDeQM9efIEY8eOhZubG549e4bFixdjy5YtiI2Nlbo0Njp7OjHPmXqOMyVrL82Uw0y9Yq7W1lZ8+OGH6O/vhyiKSExM5AOfMTNwppSHRygS6O/vR0hICDw8PHDy5Empy3EEPEJROM6UzfFlw/YiOzsbKpVK6jIYUwzOlH3ghmJjjx49wqlTp5CSkiJ1KYwpAmfKfnBDsbHU1FRkZGTAyYl/9YxZAmfKfvBfwIZOnjwJd3d3BAcHS10KY4rAmbIv3FBs6OrVqzh+/Dg8PT2RnJyM8vJyrF69WuqyGJMtzpR9Ge0qL0UQBOHvAH4gIoOvSRQE4RsAp4mo3Eo1RQDYbExNjNkLzhR7GUcZoWwE8IORj9kL4N9WqIUxJeBMsb9Q1AhFEIR3AeQCCAXgDOAagCQAxwGoiKhXEIR/AvgHgPEAvAF8A2AcgDUAegFEE1HH8/3VAIghosc2fimM2QXOFDOGokYoRFSFPw90NYAMAAUAegD8l4h6B206C8AKAO8C+A+A/xFRIIBKAGsHbXcdwDwblM6YXeJMMWMoceqVnQCqAGgB/AvAbABPhm1TQUTdALoFQegEcOL5v9cB8Bu0XRuAydYtlzG7x5liBlFiQ5kAwBXAWAB/A/Ds+c/BBr+zEgfdFzH0dzLweMYcGWeKGURRp7yeywGQBqAQwG4AdwF4mriv6QB4gQbm6DhTzCD/B76rmXsR5SzsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7, 5))\n",
    "cmhot = plt.get_cmap(\"hot\")\n",
    "\n",
    "ax1 = fig.add_subplot(121, projection='3d')\n",
    "ax1.scatter(x, y, z, p_init, s=50, c=p_init, cmap=cmhot)\n",
    "ax1.set_xlabel(\"x(m)\")\n",
    "ax1.set_ylabel(\"y(m)\")\n",
    "ax1.set_zlabel(\"z(m)\")\n",
    "ax1.set_title(\"Initial Belief\")\n",
    "\n",
    "ax2 = fig.add_subplot(122, projection='3d')\n",
    "ax2.scatter(x, y, z, p_final, s=50, c=p_final, cmap=cmhot)\n",
    "ax2.set_xlabel(\"x(m)\")\n",
    "ax2.set_ylabel(\"y(m)\")\n",
    "ax2.set_zlabel(\"z(m)\")\n",
    "ax2.set_title(\"Final Belief\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.40724787, 0.50741963, 2.74564466]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground_truth[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.75],\n",
       "       [0.75],\n",
       "       [2.25]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "com_list[np.argmax(p_hist)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
